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SPIRES-BOOKS: FIND KEYWORD BIOSTATISTICS *END*INIT* use /tmp/qspiwww.webspi1/13670.141 QRY 131.225.70.96 . find keyword biostatistics ( in books using www Cover
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Call number:9789811033070:ONLINE Show nearby items on shelf
Title:Monte-Carlo Simulation-Based Statistical Modeling
Author(s):
Date:2017
Size:1 online resource (XX, 430 p. 64 illus., 33 illus. in color p.)
Contents:Part 1: Monte-Carlo Techniques -- 1. Overview of Monte-Carlo Techniques -- 2. On Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach -- 3. Joint generation of Different Types of Data with Specified
Marginal and Association Structures for Simulation Purposes -- 4. Quantifying the Uncertainty in Optimal Experimental Schemes via Monte-Carlo Simulations -- 5. Normal and Non-normal Data Simulations for the Evaluation of Two-sample
Location Tests -- 6. Understanding dichotomization from Monte-Carlo Simulations -- Part 2: Monte-Carlo Methods in Missing Data -- 7. Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data -- 8.
Methods for Handling Incomplete Longitudinal Data due to Missing at Random Dropout -- 9. Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials -- 10. Application of Markov Chain Monte Carlo Multiple
Imputation Method to Deal with Missing Data From the Mechanism of MNAR in Sensitivity Analysis for a Longitudinal Clinical Trial -- 11. Fully Bayesian Methods for Missing Data under Ignitability Assumption -- Part 3: Monte-Carlo in
Statistical Modellings -- 12. Markov-Chain Monte-Carlo Methods in Statistical modelling -- 13. Monte-Carlo Simulation in Modeling for Hierarchical Linear Mixed Models -- 14. Monte-Carlo Simulation of Correlated Binary Responses -- 15.
Monte Carlo Methods in Financial Modeling -- 16. Bayesian Intensive Computations in Elliptical Models.
ISBN:9789811033070
Series:eBooks
Series:Springer eBooks
Series:Springer 2017 package
Keywords: Statistics , Biostatistics , Statistics , Statistics for Life Sciences, Medicine, Health Sciences , Biostatistics
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Call number:9783319458090:ONLINE Show nearby items on shelf
Title:Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry
Author(s):
Date:2017
Size:1 online resource (VIII, 295 p. 106 illus., 83 illus. in color p.)
Contents:Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies -- Automated Alignment of Mass Spectrometry Data Using Functional Geometry -- The analysis of peptide-centric mass spectrometry
data utilizing information about the expected isotope distribution -- Probabilistic and likelihood-based methods for protein identification from MS/MS data -- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data
Processing -- Mass Spectrometry Analysis Using MALDIquant -- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry -- The analysis of human serum albumin proteoforms using
compositional framework -- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection -- Statistical approach for biomarker discovery using label-free LC-MS data - an overview -- Bayesian posterior integration for
classification of mass spectrometry data -- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies -- Robust and confident predictor selection in metabolomics -- On the combination of
omics data for prediction of binary Outcomes -- Statistical analysis of lipidomics data in a case-control study
ISBN:9783319458090
Series:eBooks
Series:Springer eBooks
Series:Springer 2017 package
Keywords: Statistics , Analytical chemistry , Mathematical statistics , Bioinformatics , Biostatistics , Metabolism , Statistics , Statistics for Life Sciences, Medicine, Health Sciences , Biostatistics , Metabolomics , Computational Biology/Bioinformatics , Analytical Chemistry , Probability and Statistics in Computer Science
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Call number:1119953375:ONLINE Show nearby items on shelf
Title:Biostatistics Decoded
Author(s): Oliveira
Date:2013
Publisher:Wiley
Size:1 online resource (347 p.)
ISBN:9781119953371
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:1118645855:ONLINE Show nearby items on shelf
Title:The Biostatistics of Aging: From Gompertzian Mortality to an Index of Aging-Relatedness
Author(s): Levy
Date:2014
Publisher:Wiley
Size:1 online resource (273 p.)
ISBN:9781118645857
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0471418161:ONLINE Show nearby items on shelf
Title:Introductory Biostatistics
Author(s): Le
Date:2003
Publisher:Wiley-Interscience
Size:1 online resource (553 p.)
ISBN:9780471418160
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:047141137X:ONLINE Show nearby items on shelf
Title:Introductory Biostatistics for the Health Sciences: Modern Applications Including Bootstrap
Author(s): Chernick
Date:2003
Publisher:Wiley-Interscience
Size:1 online resource (425 p.)
ISBN:9780471411376
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0471031852:ONLINE Show nearby items on shelf
Title:Biostatistics: A Methodology for the Health Sciences, Second Edition (Online Version)
Author(s): van Belle
Date:2004
Publisher:Wiley-Interscience
Size:1 online resource (897 p.)
ISBN:9780471031857
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0470666366:ONLINE Show nearby items on shelf
Title:Understanding Biostatistics
Author(s): Klln
Date:2011
Publisher:Wiley
Size:1 online resource (391 p.)
ISBN:9780470666364
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0470641851:ONLINE Show nearby items on shelf
Title:The Essentials of Biostatistics for Physicians, Nurses, and Clinicians
Author(s): Chernick
Date:2011
Publisher:Wiley
Size:1 online resource (229 p.)
ISBN:9780470641859
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0470027266:ONLINE Show nearby items on shelf
Title:Robust Methods in Biostatistics
Author(s): Heritier
Date:2009
Publisher:Wiley
Size:1 online resource (295 p.)
ISBN:9780470027264
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0470024895:ONLINE Show nearby items on shelf
Title:Basic Biostatistics for Geneticists and Epidemiologists - A Practical Approach
Author(s): Elston
Date:2008
Publisher:Wiley
Size:1 online resource (385 p.)
ISBN:9780470024898
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0470023708:ONLINE Show nearby items on shelf
Title:Tutorials in Biostatistics V 2 - Statistical Modelling of Complex Medical Data
Author(s): D'Agostino
Date:2004
Publisher:Wiley
Size:1 online resource (497 p.)
ISBN:9780470023709
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0470023651:ONLINE Show nearby items on shelf
Title:Tutorials in Biostatistics V 1 - Statistical Methods in Clinical Studies
Author(s): D'Agostino
Date:2004
Publisher:Wiley
Size:1 online resource (465 p.)
ISBN:9780470023655
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:0470018232:ONLINE Show nearby items on shelf
Title:Bayesian Biostatistics
Author(s): Lesaffre
Date:2012
Publisher:Wiley
Size:1 online resource (535 p.)
ISBN:9780470018231
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:SPRINGER-2016-9783662493328:ONLINE Show nearby items on shelf
Title:The Cox Model and Its Applications
Author(s): Mikhail Nikulin
Date:2016
Size:1 online resource (18 p.)
Note:10.1007/978-3-662-49332-8
Contents:Introduction: Several Classical Data Examples for Survival Analysis -- Elements of Survival Analysis -- The Cox Proportional Hazards Model -- The AFT, GPH, LT, Frailty, and GLPH Models -- Cross-effect Models of Survival Functions -- The Simple Cros s-effect Model -- Goodness-of-Fit for the Cox Model -- Remarks on Computations in Parametric and Semiparametric Estimation -- Cox Model for Degradation and Failure Time Data -- References -- Index
ISBN:9783662493328
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Statistics , Epidemiology , Biostatistics , Statistics , Statistical Theory and Methods , Statistics for Life Sciences, Medicine, Health Sciences , Biostatistics , Epidemiology
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Call number:SPRINGER-2016-9783319325620:ONLINE Show nearby items on shelf
Title:Group Sequential and Confirmatory Adaptive Designs in Clinical Trials
Author(s): Gernot Wassmer
Date:2016
Size:1 online resource (301 p.)
Note:10.1007/978-3-319-32562-0
Contents:Part I Group Sequential Designs -- Repeated Significance Tests: Procedures with Equally Sized Stages -- Procedures with Unequally Sized Stages -- Confidence Intervals, p -Values, and Point Estimation -- Applications -- Part II Adaptive Confirmatory Designs with a Single Hypothesis: Adaptive Group Sequential Tests -- Decision Tools for Adaptive Designs -- Estimation and p-Values for Two-stage Adaptive Designs -- Adaptive Designs with Survival Data -- Part III Adaptive Designs with Multiple Hypothese s: Multiple Testing in Adaptive Designs -- Applications and Case Studies -- Appendix - Software for Adaptive Designs -- Index
ISBN:9783319325620
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Statistics , Pharmaceutical technology , Pharmacy , Biostatistics , Statistics , Statistics for Life Sciences, Medicine, Health Sciences , Biostatistics , Drug Safety and Pharmacovigilance , Pharmaceutical Sciences/Technology
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Call number:SPRINGER-2016-9783319312453:ONLINE Show nearby items on shelf
Title:Applied Survival Analysis Using R
Author(s): Dirk F Moore
Date:2016
Size:1 online resource (26 p.)
Note:10.1007/978-3-319-31245-3
Contents:Introduction -- Basic Principles of Survival Analysis -- Nonparametric Survival Curve Estimation -- Nonparametric Comparison of Survival Distributions -- Regression Analysis Using the Proportional Hazards Model -- Model Selection and Interpretation -- Model Diagnostics -- Time Dependent Covariates -- Multiple Survival Outcomes and Competing Risks -- Parametric Models -- Sample Size Determination for Survival Studies -- Additional Topics -- References -- Appendix A -- Index -- R Package Index
ISBN:9783319312453
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Statistics , Epidemiology , Biostatistics , Statistics , Statistics for Life Sciences, Medicine, Health Sciences , Biostatistics , Statistical Theory and Methods , Epidemiology
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Call number:SPRINGER-2016-9783319306346:ONLINE Show nearby items on shelf
Title:Introduction to Nonparametric Statistics for the Biological Sciences Using R
Author(s): Thomas W MacFarland
Date:2016
Size:1 online resource (64 p.)
Note:10.1007/978-3-319-30634-6
Contents:Chapter 1 Nonparametric Statistics for the Biological Sciences -- Chapter 2 Sign Test -- Chapter 3 Chi-Square -- Chapter 4 Mann-Whitney U Test -- Chapter 5 Wilcoxon Matched-Pairs Signed-Ranks Test -- Chapter 6 Kruskal-Wallis H-Test for Oneway Analy sis of Variance (ANOVA) by Ranks -- Chapter 7 Friedman Twoway Analysis of Variance (ANOVA) by Ranks -- Chapter 8 Spearman's Rank-Difference Coefficient of Correlation -- Chapter 9 Other Nonparametric Tests for the Biological Sciences
ISBN:9783319306346
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Statistics , Agriculture , Biostatistics , Statistics , Statistics for Life Sciences, Medicine, Health Sciences , Statistics and Computing/Statistics Programs , Biostatistics , Agriculture , Statistical Theory and Methods
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Call number:SPRINGER-2016-9783319283166:ONLINE Show nearby items on shelf
Title:An Introduction to Statistics with Python With Applications in the Life Sciences
Author(s): Thomas Haslwanter
Date:2016
Size:1 online resource (85 p.)
Note:10.1007/978-3-319-28316-6
Contents:Part I: Python and Statistics -- Why Statistics? -- Python -- Data Input -- Display of Statistical Data -- Part II: Distributions and Hypothesis Tests -- Background -- Distributions of One Variable -- Hypothesis Tests -- Tests of Means of Numerical Data -- Tests on Categorical Data -- Analysis of Survival Times -- Part III: Statistical Modelling -- Linear Regression Models -- Multivariate Data Analysis -- Tests on Discrete Data -- Bayesian Statistics -- Solutions -- Glossary -- Index
ISBN:9783319283166
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Statistics , Programming languages (Electronic computers) , Biostatistics , Computer mathematics , Statistics , Statistics and Computing/Statistics Programs , Statistics for Life Sciences, Medicine, Health Sciences , Biostatistics , Computational Science and Engineering , Programming Languages, Compilers, Interpreters
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Call number:SPRINGER-2014-9783319072128:ONLINE Show nearby items on shelf
Title:Statistical Analysis of Next Generation Sequencing Data [electronic resource]
Author(s): Somnath Datta
Dan Nettleton
Date:2014
Publisher:Cham : Springer International Publishing : Imprint: Springer
Size:1 online resource
Note:Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. Toextract signals from high-d imensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians workingwith NGS data. The topics range from basic prepr ocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn aboutthis growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected todeal with genomic data in basic biomedical research, genomic clinical trials and personalize d medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville.He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, and Elec ted Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics,Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Associationand has published research on a variety of topics in statistics, biology, and bioinformatics
Contents:Statistical Analyses of Next Generation Sequencing Data: An Overview
Using RNA
seq Data to Detect Differentially Expressed Genes
Differential Expression Analysis of Complex RNA
seq Experiments Using edgeR
Analysis of Next Generation Sequencing Data Using Integrated Nested Laplace Approximation (INLA)
Design of RNA Sequencing Experiments
Measurement, Summary, and Methodological Variation in RNA
sequencing
Functional PCA for differential expression testing with RNA
seq data
Mapping of Expression Quantitative Trait Loci using RNA
seq Data
The Role of Spike
In Standards in
ISBN:9783319072128
Series:eBooks
Series:SpringerLink
Series:Frontiers in Probability and the Statistical Sciences
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Oncology , Human genetics
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Call number:SPRINGER-2014-9783319045795:ONLINE Show nearby items on shelf
Title:Statistical Modelling in Biostatistics and Bioinformatics [electronic resource] : Selected Papers
Author(s): Gilbert MacKenzie
Defen Peng
Date:2014
Publisher:Cham : Springer International Publishing : Imprint: Springer
Size:1 online resource
Note:This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysisand multivariate sur vival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the fourareas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians suchas Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Al essandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatisticsand Bioinformatics, centred on the Universities of Limerick and Galway in Ireland a nd funded by the Science Foundation Ireland under its Mathematics Initiative
Contents:Preface
An Appreciation
John Nelder, FRS
Introduction
Survival Modelling: Hougaard
Multivariate Interval
Censored Survival Data: Parametric, Semi
Parametric and Non
Parametric Models MacKenzie and Ha
Multivariate Survival Models Based on the GTDL Lynch and MacKenzie
Frailty Models with Structural Dispersion Martinez and Hinde
Random Effects Ordinal Time Models for Grouped Toxicological Data from a Biological Control Assay
Longitudinal Modelling & Time Series: Haywood and Randal
Modelling Seasonality and Structural Breaks: Visitors to NZ and 9/11 Allais and Bosco
F
ISBN:9783319045795
Series:eBooks
Series:SpringerLink
Series:Contributions to Statistics, 1431-1968
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Bioinformatics , Statistical methods , Mathematical statistics
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Call number:SPRINGER-2014-9783319025322:ONLINE Show nearby items on shelf
Title:Introduction to Data Analysis and Graphical Presentation in Biostatistics with R [electronic resource] : Statistics in the Large
Author(s): Thomas W MacFarland
Date:2014
Publisher:Cham : Springer International Publishing : Imprint: Springer
Size:1 online resource
Note:Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressingscientific issue for those eng aged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure theaccessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples,t-Test for matched pairs, ANOVA, correlation and linear regression, and a dvice for future work
Contents:Introduction to Biostatistics and R
Data exploration, descriptive statistics and measures of central tendency
Student's t
Test for independent samples
Student's t
Test for matched pairs
One way ANOVA
Two way ANOVA
Correlation and linear regression
Future Actions and Next Steps
ISBN:9783319025322
Series:eBooks
Series:SpringerLink
Series:SpringerBriefs in Statistics, 2191-544X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2014-9783319024028:ONLINE Show nearby items on shelf
Title:Foundations of Applied Statistical Methods [electronic resource]
Author(s): Hang Lee
Date:2014
Publisher:Cham : Springer International Publishing : Imprint: Springer
Size:1 online resource
Note:This is a text in methods of applied statistics for researchers who design and conduct experiments, perform statistical inference, and write technical reports. These research activities rely on an adequate knowledge of appliedstatistics. The reader b oth builds on basic statistics skills and learns to apply them to applicable scenarios without over-emphasis on the technical aspects. Demonstrations are a very important part of this text. Mathematicalexpressions are exhibited only if they are defined or intuitively comprehensible. This textmay be used as a self review guidebook for applied researchers or as an introductory statistical methods textbook for students not majoring instatistics. Discussion includes essential probability models, inference of means, proportions, correlations and regressions, methods for censored survival time data analysis, and sample size determination. The authorhas over twentyyears of experienceapplying statistical methods tostudy design and data analysisin collaborative me dical research setting as well as on teaching.He received hisPhDfrom the Department of Preventive Medicine at the Universityof Southern California andpost-doctoral training at Harvard Department of Biostatistics. Hang Leehas held faculty appointments at t he UCLASchool of Medicine and Harvard Medical School. He is currently a biostatisticsfacultymember at Massachusetts General Hospital and Harvard Medical Schoolin Boston, Massachusetts, USA.
Contents:Warming Up
Descriptive Statistics and Essential Probability Models
Statistical Inference Focusing on a Single Mean or Proportion
Inference Using t
tests for Comparing Two Means
Inference Using Analysis of Variance for Comparing Multiple Means
Inference Using Correlation and Regression
Normal Distribution Assumption Free Non
Parametric Inference
Methods for Censored Survival Time Data Analysis and Inference
Sample Size Determination for Inference
Review Exercise Problems
Probability of Standard Normal Distribution
Percentiles of t
Distributions
Upper 95th and 99
ISBN:9783319024028
Series:eBooks
Series:SpringerLink
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2014-9783319015958:ONLINE Show nearby items on shelf
Title:Data Analysis, Machine Learning and Knowledge Discovery [electronic resource]
Author(s): Myra Spiliopoulou
Lars Schmidt-Thieme
Ruth Janning
Date:2014
Publisher:Cham : Springer International Publishing : Imprint: Springer
Size:1 online resource
Note:Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be appliedto a vast set of app lications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning andknowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012
Contents:AREA Statistics and Data Analysis: Classifcation, Cluster Analysis, Factor Analysis and Model Selection
AREA Machine Learning and Knowledge Discovery: Clustering, Classifiers, Streams and Social Networks
AREA Data Analysis and Classification in Marketing
AREA Data Analysis in Finance
AREA Data Analysis in Biostatistics and Bioinformatics
AREA Interdisciplinary Domains: Data Analysis in Music, Education and Psychology
LIS Workshop: Workshop on Classification and Subject Indexing in Library and Information Science
ISBN:9783319015958
Series:eBooks
Series:SpringerLink
Series:Studies in Classification, Data Analysis, and Knowledge Organization, 1431-8814
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Statistical methods , Mathematical statistics , Marketing , Philosophy (General)
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Call number:SPRINGER-2013-9783319000350:ONLINE Show nearby items on shelf
Title:Algorithms from and for Nature and Life [electronic resource] : Classification and Data Analysis
Author(s): Berthold Lausen
Dirk Van den Poel
Alfred Ultsch
Date:2013
Publisher:Cham : Springer International Publishing : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This volume provides approaches and solutions to challenges occurring at the interface of research fields such as, e.g., data analysis, data mining and knowledge discovery, computer science, operations research, and statistics. Inaddition to theory-o riented contributions various application areas are included. Moreover, traditional classification research directions concerning network data, graphs, and social relationships as well as statistical musicologydescribe examples for current interest fields tackled by the authors. The book comprises a total of 55 selected papers presented at the Joint Conference of the German Classification Society (GfKl),the German Association for PatternRecognition (DAGM), and the Symposium of the International Federation of Classification Societies (IFCS) in 2011
Note:Springer eBooks
Contents:Invited
Clustering and Unsupervised Learning
Statistical Data Analysis, Visualization and Scaling
Bioinformatics and Biostatistics
Archaeology and Geography, Psychology and Educational Sciences
Text Mining, Social Networks and Clustering
Banking and Finance
Marketing and Management
Music Classification Workshop
ISBN:9783319000350
Series:e-books
Series:SpringerLink (Online service)
Series:Studies in Classification, Data Analysis, and Knowledge Organization, 1431-8814
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Social sciences Data processing , Mathematical statistics , Economics Statistics , Operations research
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Call number:SPRINGER-2013-9781461493327:ONLINE Show nearby items on shelf
Title:Topics from the 8th Annual UNCG Regional Mathematics and Statistics Conference [electronic resource]
Author(s): Jan Rycht
Sat Gupta
Ratnasingham Shivaji
Maya Chhetri
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:The Annual University of North Carolina Greensboro Regional Mathematics and Statistics Conference (UNCG RMSC) has provided a venue for student researchers to share their work since 2005. The 8th Conference took place on November3, 2012. The UNCG-RMSC conference established a tradition of attracting active researchers and their faculty mentors from NC and surrounding states. The conference is specifically tailored for students to present the results of theirresearch and to allow participants to intera ct with and learn from each other. This type of engagement is truly unique. The broad scope of UNCG-RMSC includes topics in applied mathematics, number theory, biology, statistics,biostatistics and computer sciences.
Note:Springer eBooks
Contents:Preface
Promoting Undergraduate Research in Mathematics
A program Making a Difference in Quantitative Sciences
Quantitative Methods in Biomedical Applications: Creative Inquiry and Digital Learning Environments to Engage and Mentor STEM Students in Mathematics
ISBN:9781461493327
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Proceedings in Mathematics & Statistics, 2194-1009 : v64
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2013-9781461487159:ONLINE Show nearby items on shelf
Title:Statistical Analysis of Panel Count Data [electronic resource]
Author(s): Jianguo Sun
Xingqiu Zhao
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
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Note:Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multipletimes or repeatedly. Exa mples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield eventhistory data too such as demographic studies, eco nomic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This bookcollects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of thereader, the applications of the methods to real data are also discussed along with numerical calculation s. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature onthe analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count datato answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.
Note:Springer eBooks
Contents:Introduction
Poisson Models and Parameter Inference
Nonparametric Estimation
Nonparametric Comparison of Point Processes
Regression Analysis of Panel Count Data I and II
Analysis of Multivariate Panel Count Data
Other Topics
Some Sets of Data
References
Index
ISBN:9781461487159
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776 : v80
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2013-9781461474289:ONLINE Show nearby items on shelf
Title:Statistical Methods for Dynamic Treatment Regimes [electronic resource] : Reinforcement Learning, Causal Inference, and Personalized Medicine
Author(s): Bibhas Chakraborty
Erica E.M Moodie
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
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Note:Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. Thisvolume demonstrates thes e methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medicalparadigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, andtechnical reports with the goal of orienting researchers to the fiel d. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementarycalculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where codedoes not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowled ge of statistical programming could implement the methods from scratch. This will be an important volume for a widerange of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also findmaterial in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies
Note:Springer eBooks
Contents:Introduction
The Data: Observational Studies and Sequentially Randomized Trials
Statistical Reinforcement Learning
Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes
Estimation of Optimal DTRs by Directly Modeling Regimes
G
computation: Parametric Estimation of Optimal DTRs
Estimation DTRs for Alternative Outcome Types
Inference and Non
regularity
Additional Considerations and Final Thoughts
Glossary
Index
References
ISBN:9781461474289
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Medical records Data processing
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Call number:SPRINGER-2013-9781461461142:ONLINE Show nearby items on shelf
Title:Sequential Experimentation in Clinical Trials [electronic resource] : Design and Analysis
Author(s): Jay Bartroff
Tze Leung Lai
Mei-Chiung Shih
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
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Note:This book presents an integrated methodology for sequential experimentation in clinical trials. The methodology allows sequential learning during the course of a trial to improve the efficiency of the trial design, which oftenlacks adequate informati on at the planning stage. Adaptation via sequential learning of unknown parameters is a central idea not only in adaptive designs of confirmatory clinical trials but also in the theory of optimal nonlinearexperimental design, which the book covers as intr oductory material. Other introductory topics for which the book provides preparatory background include sequential testing theory, dynamic programming and stochastic optimization,survival analysis and resampling methods. In this way, the book gives a self -contained and thorough treatment of group sequential and adaptive designs, time-sequential trials with failure-time endpoints, and statistical inference atthe conclusion of these trials. The book can be used for graduate courses in sequential analysis, c linical trials, and biostatistics, and also for short courses on clinical trials at professional meetings. Each chapter ends withsupplements for the reader to explore related concepts and methods, and problems which can be used for exercises in graduate c ourses. Jay Bartroff is Associate Professor of Mathematics at the University of Southern California where heis a member of the Laboratory of Applied Pharmacokinetics at the USC Keck School of Medicine. He is a leading expert on group sequential and multis tage adaptive statistical procedures and their applications to clinical trial designs,and he is a sought-after consultant in academia and industry. Tze Leung Lai is Professor of Statistics, and by courtesy, of Health Research and Policy and of the Institu te of Computational and Mathematical Engineering at StanfordUniversity, where he is the Director of the Financial and Risk Modeling Institute and Co-director of the Biostatistics Core at the Stanford Ca
Note:Springer eBooks
Contents:Introduction
Nonlinear Regression, Experimental Design, and Phase I Clinical Trials
Sequential Testing Theory and Stochastic Optimization over Time
Group Sequential Design of Phase II and III Trials
Sequential Methods for Vaccine Safety Evaluation and Surveillance in Public Health
Time
Sequential Design of Clinical Trials with Failure
Time Endpoints
Confidence Intervals and p
Values
Adaptive Design of Confirmatory Trials
References
ISBN:9781461461142
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397 : v298
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2013-9781461458388:ONLINE Show nearby items on shelf
Title:Optimization [electronic resource]
Author(s): Kenneth Lange
Date:2013
Edition:2nd ed. 2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a balance betweenpresentation of mathematical theo ry and development of numerical algorithms. Building on students skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statisticalapplications will be especially appealing to graduate stude nts of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics whowant to see rigorous mathematics combined with real applications. In this second edition, the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and thecalculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the F enchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penaltymethods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dime nsions
Note:Springer eBooks
Contents:Elementary Optimization
The Seven Cs of Analysis
The Gauge Integral
Differentiation
Karush
Kuhn
Tucker Theory
Convexity
Block Relaxation
The MM Algorithm
The EM Algorithm
Newtons Method and Scoring
Conjugate Gradient and Quasi
Newton
Analysis of Convergence
Penalty and Barrier Methods
Convex Calculus
Feasibility and Duality
Convex Minimization Algorithms
The Calculus of Variations
Appendix: Mathematical Notes
References
Index
ISBN:9781461458388
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X : v95
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical optimization , Mathematical statistics , Operations research
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Call number:SPRINGER-2013-9781461456964:ONLINE Show nearby items on shelf
Title:Applied Bayesian Statistics [electronic resource] : With R and OpenBUGS Examples
Author(s): Mary Kathryn Cowles
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics andStatistics and studen ts in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, andinterpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to buildunderstanding of the concepts and procedures required to answer re al questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchicalmodels, and assessing Markov chain Monte Carlo output. Mary Kathryn(Kate) Cowles taught Suzu ki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics,with application to environmental science. She is on the faculty of Statistics at The University of Iowa
Note:Springer eBooks
Contents:What is Bayesian statistics?
Review of probability
Introduction to one
parameter models
Inference for a population proportion
Special considerations in Bayesian inference
Other one
parameter models and their conjugate priors
More realism please: Introduction to multiparameter models
Fitting more complex Bayesian models: Markov chain Monte Carlo
Hierarchical models, and more on convergence assessment
Regression and hierarchical regression models
Model Comparison, Model Checking, and Hypothesis Testing
References
Index
ISBN:9781461456964
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X : v98
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2013-9781461452454:ONLINE Show nearby items on shelf
Title:Proceedings of the Fourth Seattle Symposium in Biostatistics: Clinical Trials [electronic resource]
Author(s): Thomas R Fleming
Bruce S Weir
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
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Note:This volume contains a selection of chapters based on papers presented at the Fourth Seattle Symposium on Biostatistics: Clinical Trials. These biostatistical symposiums, which unite leading researchers every five years,represent important developmen ts in field. The Fourth Seattle Symposium was held in 2010 to celebrate the 40th anniversary of the University of Washington School of Public Health and Community Medicine. The Symposium featured keynotelectures by Robert ONeill, Ross Prentice and Robert Temple, as well as invited talks by Jesse Berlin, Christy Chuang-Stein, David DeMets, Bill DuMouchel, Susan Ellenberg, Thomas Fleming, Laurence Freedman, Margaret Pepe, Steve Self,Richard Simon, Bruce Weir, John Whittaker and Janet Wittes. Invited panelis ts included Jesse Berlin, Bruce Binkowitz, Christy Chuang-Stein, Bill DuMouchel, Susan Ellenberg, Thomas Fleming, Henry Fuchs, Dominic Labriola, RobertONeill, Robert Temple and Janet Wittes. The thoroughly peer-reviewed papers and material from short cour ses that are showcased in this volume represent the theme of the symposium, clinical trials. These papers encompass recentmethodological advances on several important topics, summaries of the state of the art of methodology in key areas of clinical trials , as well as innovative applications of the existing theory and methods. This volume will be avaluable reference for researchers and practitioners in the field of clinical trials
Note:Springer eBooks
ISBN:9781461452454
Series:e-books
Series:SpringerLink (Online service)
Series:Lecture Notes in Statistics, 0930-0325 : v1205
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2013-9781461448549:ONLINE Show nearby items on shelf
Title:SAS for Epidemiologists [electronic resource] : Applications and Methods
Author(s): Charles DiMaggio
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
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Note:Springer 2013 e-book collections
Note:This comprehensive text covers the use of SAS for epidemiology and public health research. Developed with students in mind and from their feedback, the text addresses this material in a straightforward manner with a multitude ofexamples. It is direct ly applicable to students and researchers in the fields of public health, biostatistics and epidemiology. Through a hands on approach to the use of SAS for a broad number of epidemiologic analyses, readerslearn techniques for data entry and cleaning, cate gorical analysis, ANOVA, and linear regression and much more. Exercises utilizing real-world data sets are featured throughout the book. SAS screen shots demonstrate the steps forsuccessful programming. SAS (Statistical Analysis System) is an integrated s ystem of software products provided by the SAS institute, which is headquartered in California. It provides programmers and statisticians the ability to engagein many sophisticated statistical analyses and data retrieval and mining exercises. SAS is widel y used in the fields of epidemiology and public healthresearch, predominately due to its ability to reliably analyze very largeadministrative data sets, as well as more commonly encountered clinical trial and observational research data.
Note:Springer eBooks
Contents:Introduction
The SAS Environment
Working with SAS Data
Preliminary Procedures
Manipulating Data
Descriptive Statistics
Histograms and Plots
Categorical Data Analysis I and II
Cleaning and Assessing Continuous Data using MEANS
ANOVA
Correlation
Linear Regression
Regression Diagnostics
Solutions
ISBN:9781461448549
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2013-9781461439004:ONLINE Show nearby items on shelf
Title:Linear Mixed-Effects Models Using R [electronic resource] : A Step-by-Step Approach
Author(s): Andrzej Gaecki
Tomasz Burzykowski
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
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Note:Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics,educational measuremen t, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. Tohelp readers to get familiar with the feature s of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software andapplications. It is built up incrementally, starting with a summa ry of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach isused to describe the R tools for LMMs. All the classes of linear models presented in t he book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariancestructure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text. Andrzej Gaeckiis a Research Professor in the Division of Geriatric Medicine, Department of Internal Medicine, and Institute of Gerontology at the University of Michigan Medical School, and is Research Scientist in the Department of Biostatistics atthe University of Michigan School of Public Health. He earned his M.Sc. in applied mathematics (1977) from the Technical University of Warsaw, Poland, an d an M.D. (1981) from the Medical University of Warsaw. In 1985 he earned a Ph.D.in epidemiology from the Institute of Mother and Child Care in Warsaw (Poland). He is a member of the Editorial Board o
Note:Springer eBooks
Contents:Introduction
Linear Models for Independent Observations
Linear Fixed
effects Models for Correlated Data
Linear Mixed
effects Models
ISBN:9781461439004
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2013-9781461436492:ONLINE Show nearby items on shelf
Title:Strength in Numbers: The Rising of Academic Statistics Departments in the U. S. [electronic resource]
Author(s): Alan Agresti
Xiao-Li Meng
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a setof memoirs, one for each de partment in the U.S. created by the mid-1960s. The memoirs describe key aspects of the departments history -- its founding, its growth, key people in its development, success stories (such as majorresearch accomplishments) and the occasional failure story , PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Readhere all about how departments such as at Berkeley, Chicago, Harvard, and Stanfor d started and how they got to where they are today. The book should also be of interest to scholars in the field of disciplinary history
Note:Springer eBooks
Contents:Statistics as an Academic Discipline
Carnegie
Mellon
Columbia University
Cornell University
Florida State University
George Washington University
Harvard University
Harvard University
Iowa State University
Johns Hopkins University
Kansas State University
Michigan State University
North Carolina State
Oregon State University
Penn State University
Princeton University
Purdue University
Rutgers University
Southern Methodist University
Stanford University
SUNY at Buffalo
Texas A&M
University of California
University of Chicago
ISBN:9781461436492
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Science History , Public health , Computer science , Education, Higher , Social sciences
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Call number:SPRINGER-2013-9781441909251:ONLINE Show nearby items on shelf
Title:Bayesian and Frequentist Regression Methods [electronic resource]
Author(s): Jon Wakefield
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
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Note:Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensivecombination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementarycomponents of the discussion. In particular, metho ds are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines.While the philosophy behind each approach is discussed, the book is not i deological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approachescan lead to broadly similar conclusions. To use this text, the reader requires a basic understan ding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author'sexperience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the book
Note:Springer eBooks
Contents:Introduction
Frequentist Inference
Bayesian Inference
Linear Models
Binary Data Models
General Regression Models
ISBN:9781441909251
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2012-9781461422457:ONLINE Show nearby items on shelf
Title:Analysis of Genetic Association Studies [electronic resource]
Author(s): Gang Zheng
Yaning Yang
Xiaofeng Zhu
Robert C Elston
Date:2012
Publisher:Boston, MA : Springer US
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Analysis of Genetic Association Studies is both a graduate level textbook in statistical genetics and genetic epidemiology, and a reference book for the analysis of genetic association studies. Students, researchers, andprofessionals will find the to pics introduced in Analysis of Genetic Association Studies particularly relevant. The book is applicable to the study of statistics, biostatistics, genetics and genetic epidemiology. In addition toproviding derivations, the book uses real examples and si mulations to illustrate step-by-step applications. Introductory chapters on probability and genetic epidemiology terminology provide the reader with necessary backgroundknowledge. The organization of this work allows for both casual reference and close st udy.
Note:Springer eBooks
ISBN:9781461422457
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2012-9781461420354:ONLINE Show nearby items on shelf
Title:Competing Risks and Multistate Models with R [electronic resource]
Author(s): Jan Beyersmann
Arthur Allignol
Martin Schumacher
Date:2012
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:Springer 2013 e-book collections
Note:Competing Risks and Multistate Models with R covers models that generalize the analysis of time to a single event (survival analysis) to analyzing the timing of distinct terminal events (competing risks) and possible intermediateevents (multistate mo dels). Both R and multistate methods are promoted with a focus on non- and semiparametric methods. This book explains hazard-based analyses of competing risks and multistate data with R. Special emphasis isplaced on the interpretation of the results. A u nique feature of this book is that readers are encouraged to simulate their own data based on the transition hazards only, which are the key quantities of the subsequent analyses. Thissimulation-based approach is supplemented with real data examples from studies in clinical medicine where the authors have been involved. This book is aimed at data analysts, with a background in standard survival analysis, who wishto understand, analyse and interpret more complex event histories with R. It is also suitable for graduate courses in biostatistics, statistics and epidemiological methods. The real data examples, R packages, and the entire R code usedin the book are available online. The authors are affiliated with the Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg and the Freiburg Center for Data Analysis and Modelling, University ofFreiburg, Germany. Jan Beyersmann is Senior Statistician and serves on the editorial board of Statistics in Medicine. Arthur Allignol is Statistician and has contributed several R packages on competing risks and multistate models.Martin Schumacher is Professor of Biostatistics and Director of the Institute of Medical Biometry and Medical Informatics, Freiburg. He has been involved in theor etical developments as well as in practical applications of survivalanalyses and their extensions over many years
Note:Springer eBooks
Contents:Data examples
An informal introduction to hazard
based analyses
Competing risks
Multistate modelling of competing risks
Nonparametric estimation
Proportional hazards models
Nonparametric hypothesis testing
Further topics in competing risks
Multistate models and their connection to competing risks
Nonparametric estimation
Proportional transition hazards models
Time
dependent covariates and multistate models
Further topics in multistate modeling
ISBN:9781461420354
Series:e-books
Series:SpringerLink (Online service)
Series:Use R!
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2012-9781461413530:ONLINE Show nearby items on shelf
Title:Regression Methods in Biostatistics [electronic resource] : Linear, Logistic, Survival, and Repeated Measures Models
Author(s): Eric Vittinghoff
David V Glidden
Stephen C Shiboski
Charles E McCulloch
Date:2012
Edition:2nd ed. 2012
Publisher:Boston, MA : Springer US
Size:1 online resource
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Note:Springer 2013 e-book collections
Note:This new edition provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Coxmodel for right-censor ed survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they havein common. The authors point out the many-s hared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding,mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics isassumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further readingare provided. For many students and researchers learning to use these methods, this one book may be all they need t o conduct and interpret multipredictor regression analyses. In the second edition, the authors have substantiallyexpanded the core chapters, including new coverage of exact, ordinal, and multinomial logistic models, discrete time and competing risks survi val models, within and between effects in longitudinal models, zero-inflated Poisson andnegative binomial models, cross-validation for prediction model selection, directed acyclic graphs, and sample size, power and minimum detectable effect calculations S tata code is also updated. In addition, there are new chapters onmethods for strengthening causal inference, including propensity scores, marginal structural models, and instrumental variables,and on
Note:Springer eBooks
Contents:Introduction
Exploratory and Descriptive Methods
Basic Statistical Methods
Linear Regression
Logistic Regression
Survival Analysis
Repeated Measures Analysis
Generalized Linear Models
Strengthening Casual Inference
Predictor Selection
Complex Surveys
Summary
ISBN:9781461413530
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Epidemiology
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Call number:SPRINGER-2011-9781461404996:ONLINE Show nearby items on shelf
Title:Generalized Estimating Equations [electronic resource]
Author(s): Andreas Ziegler
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Generalized estimating equations have become increasingly popular in biometrical, econometrical, and psychometrical applications because they overcome the classical assumptions of statistics, i.e. independence and normality, whichare too restrictive for many problems. Therefore, the main goal of this book is to give a systematic presentation of the original generalized estimating equations (GEE) and some of its further developments. Subsequently, the emphasisis put on the unification of various GEE a pproaches. This is done by the use of two different estimation techniques, the pseudo maximum likelihood (PML) method and the generalized method of moments (GMM). The author details thestatistical foundation of the GEE approach using more general estimati on techniques. The book could therefore be used as basis for a course to graduate students in statistics, biostatistics, or econometrics, and will be useful topractitioners in the same fields
Note:Springer eBooks
Contents:The linear exponential family
The quadratic exponential family
Generalized linear models
Maximum likelihood method
Quasi maximum likelihood method
Pseudo maximum likelihood method based on the linear exponential family
Quasi generalized pseudo maximum likelihood method based on the linear exponential family
Algorithms for solving the generalized estimating equations and the relation to the jack
knife estimator of variance
Pseudo maximum likelihood estimation based on the quadratic exponential family
Generalized method of moment estimation
ISBN:9781461404996
Series:e-books
Series:SpringerLink (Online service)
Series:Lecture Notes in Statistics, 0930-0325 : v204
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2011-9781461403944:ONLINE Show nearby items on shelf
Title:Statistics for Bioengineering Sciences [electronic resource] : With MATLAB and WinBUGS Support
Author(s): Brani Vidakovic
Date:2011
Edition:1
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing engineering fields, bioengineering and biomedical engineering, while implementing software that engineers are familiar with.The author integrate s introductory statistics for engineers and introductory biostatistics as a single textbook heavily oriented to computation and hands on approaches. For example, topics ranging from the aspects of disease anddevice testing, Sensitivity, Specificity and RO C curves, Epidemiological Risk Theory, Survival Analysis, or Logistic and Poisson Regressions are covered. In addition to the synergy of engineering and biostatistical approaches, thenovelty of this book is in the substantial coverage of Bayesian approach es to statistical inference. Many examples in this text are solved using both the traditional and Bayesian methods, and the results are compared and commented
Note:Springer eBooks
Contents:Introduction
The Sample and Its Properties
Probability, Conditional Probability, and Bayes' Rule
Sensitivity, Specificity, and Relatives
Random Variables
Normal Distribution
Point and Interval Estimators
Bayesian Approach to Inference
Testing Statistical Hypotheses
Two Samples
ANOVA and Elements of Experimental Design
Distribution
Free Tests
Goodness
of
Fit Tests
Models for Tables
Correlation
Regression
Regression for Binary and Count Data
Inference for Censored Data and Survival Analysis
Bayesian Inference Using Gibbs Sampling
BUGS Proje
ISBN:9781461403944
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2011-9781441998422:ONLINE Show nearby items on shelf
Title:Modern Issues and Methods in Biostatistics [electronic resource]
Author(s): Mark Chang
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:Classic biostatistics, a branch of statistical science, has as its main focus the applications of statistics in public health, the life sciences, and the pharmaceutical industry. Modern biostatistics, beyond just a simpleapplication of statistics, is a confluence of statistics and knowledge of multiple intertwined fields. The application demands, the advancements in computer technology, and the rapid growth of life science data (e.g., genomics data)have promoted the formation of modern biostatistics. There are at least three characteristics of modern biostatistics: (1) in-depth engagement in the application fields that require penetration of knowledge across several fields, (2)high-level complexity of data because they are longitudinal, incomplete, o r latent because they are heterogeneous due to a mixture of data or experiment types, because of high-dimensionality, which may make meaningful reductionimpossible, or because of extremely small or large size and (3) dynamics, the speed of development in methodology and analyses, has to match the fast growth of data with a constantly changing face. This book is written forresearchers, biostatisticians/statisticians, and scientists who are interested in quantitative analyses. The goal is to introduce moder n methods in biostatistics and help researchers and students quickly grasp key concepts and methods.Many methods can solve the same problem and many problems can be solved by the same method, which becomes apparent when those topics are discussed inthis s ingle volume
Note:Springer eBooks
Contents:Multiple
Hypothesis Testing Strategy
Pharmaceutical Decision and Game Theory
Noninferiority Trial Design
Adaptive Trial Design
Missing Data Imputation and Analysis
Multivariate and Multistage Survival Data Modeling
Meta
analysis
Data Mining and Signal Detection
Monte Carlo Simulation
Bayesian Methods and Applications
ISBN:9781441998422
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Mathematics , Engineering
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Call number:SPRINGER-2011-9781441983428:ONLINE Show nearby items on shelf
Title:Dynamic Mixed Models for Familial Longitudinal Data [electronic resource]
Author(s): Brajendra C Sutradhar
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model thelongitudinal correlati ons for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized todevelop theoretically sound inference techniq ues such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existingworking correlations based GEE (generalized estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is notsuitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal datacollected from the members of a large number of independent families by using the class of auto-correlation structures conditiona l on the random effects. The book also provides models and inferences for discrete longitudinal data inthe adaptive clinical trial set up. The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special caresfor mathematics behind the correlation models, it also presents the illustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics andeconometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial Universi
Note:Springer eBooks
Contents:Introduction
Overview of Linear Fixed Models for Longitudinal Data
Overview of Linear Mixed Models for Longitudinal Data
Familial Models for Count Data
Familial Models for Binary Data
Longitudinal Models for Count Data
Longitudinal Models for Binary Data
Longitudinal Mixed Models for Count Data
Longitudinal Mixed Models for Binary Data
Familial Longitudinal Models for Count Data
Familial Longitudinal Models for Binary Data
ISBN:9781441983428
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Epidemiology , Biometrics , Statistical methods , Mathematical statistics , Econometrics , Social sciences Methodology
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Call number:SPRINGER-2011-9781441979766:ONLINE Show nearby items on shelf
Title:Numerical Ecology with R [electronic resource]
Author(s): Daniel Borcard
Francois Gillet
Pierre Legendre
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
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Note:Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the usersthrough the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with theconstruction of associationmatrices, then addre sses three families of methods: clustering, unconstrained and canonical ordination,and spatial analysis. All the necessary data files, the scripts used in the chapters, as well as theextra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site(http://adn.biol.umontreal.ca/~numericalecology/numecolR/). This book is aimed at professionalresearchers, practitioners, graduate students and teachers in ecology, environmental science and enginee ring, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background ingeneral and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people w illing to accompany their disciplinary learning with practical applications. People from other fields (e.g.geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presente d in this book. The three authors teach numerical ecology, both theoretical andpractical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and E cology and researcher in Numerical Ecology at Universitde Montral, Qubec, Canada. Franois Gillet is professor of Community Ecology and Ecological Modelling at Universit de Franche-Comt,
Note:Springer eBooks
Contents:Introduction
Exploratory data analysis
Association measures and matrices
Cluster analysis
Unconstrained ordination
Canonical ordination
Spatial analysis of ecological data
ISBN:9781441979766
Series:e-books
Series:SpringerLink (Online service)
Series:Use R
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Epidemiology , Statistical methods , Ecology , Forests and forestry
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Call number:SPRINGER-2011-9781441977878:ONLINE Show nearby items on shelf
Title:Statistics and Data Analysis for Financial Engineering [electronic resource]
Author(s): David Ruppert
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration ofconcepts with financial marke ts and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduatetextbook Statistics and Finance: An Introduction, thi s book differs from that earlier volume in several important aspects: it is graduate-level computations and graphics are done in R and many advanced topics are covered, forexample, multivariate distributions, copulas, Bayesian computations, VaR and expect ed shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure tofinance is helpful. David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statist ical Science, School of Operations Research and Information Engineering, Cornell University, where he teaches statistics andfinancial engineering and is a member of the Program in Financial Engineering. His research areas include asymptotic theory, semipa rametric regression, functional data analysis, biostatistics, model calibration, measurement error, andastrostatistics. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association a nd the Institute of Mathematical Statistics and won the Wilcoxon prize. He isEditor of the Electronic Journal of Statistics, former Editor of the Institute of Mathematical Statistics's Lecture Notes--Monographs Series, and former Associate Editor of sever al major statistics journals. Professor Ruppert haspublished over 100 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametri
Note:Springer eBooks
Contents:Introduction
Returns
Fixed income securities
Exploratory data analysis
Modeling univariate distributions
Resampling
Multivariate statistical models
Copulas
Time series models: basics
Time series models: further topics
Portfolio theory
Regression: basics
Regression: troubleshooting
Regression: advanced topics
Cointegration
The capital asset pricing model
Factor models and principal components
GARCH models
Risk management
Bayesian data analysis and MCMC
Nonparametric regression and splines
ISBN:9781441977878
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Economics Statistics
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Call number:SPRINGER-2011-9781441973382:ONLINE Show nearby items on shelf
Title:The Fundamentals of Modern Statistical Genetics [electronic resource]
Author(s): Nan M Laird
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendels first experiments to genome-wideassociation studies, the book de scribes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation,linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intendedaudience is statisticians, biostatisticians, epidemiologists and quantitatively- orien ted geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or topursue research in methodology. A background in intermediate level statistical methods is required. The aut hors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels.No background in genetics is assumed. Dr. Laird is a Professor of Biostatistics in the Biostatistics Department at the Harvard Sc hool of Public Health. Dr. Laird has contributed to methodology in many different fields, includingmissing data, EM-algorithm, meta-analysis, statistical genetics, and has coauthored a book with Garrett Fitzmaurice and James Ware on Applied Longitudinal A nalysis. She is the recipient of many awards and prizes, including Fellow ofthe American Statistical Association, the American Association for the Advancement of Science, the Florence Nightingale Award, and the Janet Norwood Award. Dr. Lange is an Associa te Professor in the Biostatistics Department at theHarvard School of Public Health. After his PhD in Statistics at the University of Reading (UK), he has worked extensively in the field of statistica
Note:Springer eBooks
Contents:Introduction to statistical genetics and background in molecular genetics
Principles of inheritance: mendel's laws and genetic models
Some basic concepts from population genetics
Aggregation, heritability and segregation analysis: modeling genetic inheritance without genetic data
The general concepts of gene mapping: Linkage, association, linkage disequilibrium and marker maps
Basic concepts of linkage analysis
The basics of genetic association analysis
Population substructure in association studies
Association analysis in family designs
Advanced topics
Genome wid
ISBN:9781441973382
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Human genetics , Epidemiology , Biometrics , Statistical methods
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Call number:SPRINGER-2010-9783642107481:ONLINE Show nearby items on shelf
Title:Analysing Seasonal Health Data [electronic resource]
Author(s): Adrian G Barnett
Annette J Dobson
Date:2010
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Seasonal patterns have been found in a remarkable range of health conditions, including birth defects, respiratory infections and cardiovascular disease. Accurately estimating the size and timing of seasonal peaks in diseaseincidence is an aid to und erstanding the causes and possibly to developing interventions. With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonaleffects on health is timely. This is the first book on statistical methods for seasonal data written for a health audience. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstratesappropriate techniques for summarising and modelling these data. It has a practical focus and uses interesting examples to motivate and illustrate the methods. The statistical procedures and example data sets are available in an Rpackage called season. Adrian Barnett is a senior research fellow at Queensland University of Techn ology, Australia. Annette Dobson is a Professor of Biostatistics at The University of Queensland, Australia. Both are experiencedmedical statisticians with a commitment to statistical education and have previously collaborated in research in the methodolo gical developments and applications of biostatistics, especially to time series data. Among other projects,they worked together on revising the well-known textbook An Introduction to Generalized Linear Models, third edition, Chapman Hall/CRC, 2008. In the ir new book they share their knowledge of statistical methods for examiningseasonal patterns in health
Note:Springer eBooks
Contents:Introduction
Introduction to Seasonality
Cosinor
Decomposing Time Series
Controlling for Season
Clustered Seasonal Data
References
Index
ISBN:9783642107481
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Environmental Medicine
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Call number:SPRINGER-2010-9781441971708:ONLINE Show nearby items on shelf
Title:Regression with Linear Predictors [electronic resource]
Author(s): Per Kragh Andersen
Lene Theil Skovgaard
Date:2010
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. This is done by focusing on the manycommon aspects of these mod els, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choicesof parametrization of the effects of the covaria tes (categorical as well as quantitative) and interaction among these are elaborated upon. The merits and drawbacks of different link functions relating the linear predictor to theoutcome are discussed with an emphasis on interpretational issues, and the fact that different research questions arise from adding or deleting covariates from the model is emphasized in both theory and practice. Regression models witha linear predictor are commonly used in fields such as clinical medicine, epidemiology, and pub lic health, and the book, including its many worked examples, builds on the authors' more than thirty years of experience as teachers,researchers and consultants at a biostatistical department. The book is well-suited for readers without a solid mathemati cal background and is accompanied by Web pages documenting in R, SAS, and STATA, the analyses presented throughoutthe text. The authors are since 1978 affiliated with the Department of Biostatistics, University of Copenhagen. Per Kragh Andersen is profess or he is a co-author of the Springer book Statistical Models Based on Counting Processes,and has served on editorial boards on several statistical journals. Lene Theil Skovgaard is associate professor she has considerable experience as teacher and consult ant, and has served on the editorial board of Biometrics
Note:Springer eBooks
Contents:Introduction
Statistical models and inference
Regression models with one categorical covariate
A single quantitative covariate
Multiple regression, the linear predictor
Model building
Other link functions and other types of outcome
Further topics
ISBN:9781441971708
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776 : v0
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2010-9781441971654:ONLINE Show nearby items on shelf
Title:Applied Probability [electronic resource]
Author(s): Kenneth Lange
Date:2010
Edition:Second
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Applied Probability presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the biological sciences. It can serve as a textbook for graduatestudents in applied ma thematics, biostatistics, computational biology, computer science, physics, and statistics. Readers should have a working knowledge of multivariate calculus, linear algebra, ordinary differential equations, andelementary probability theory. Chapter 1 revi ews elementary probability and provides a brief survey of relevant results from measure theory. Chapter 2 is an extended essay on calculating expectations. Chapter 3 deals with probabilisticapplications of convexity, inequalities, and optimization theory. Chapters 4 and 5 touch on combinatorics and combinatorial optimization. Chapters 6 through 11 present core material on stochastic processes. If supplemented withappropriate sections from Chapters 1 and 2, there is sufficient material for a traditional se mester-long course in stochastic processes covering the basics of Poisson processes, Markov chains, branching processes, martingales, anddiffusion processes. The second edition adds two new chapters on asymptotic and numerical methods and an appendix that separates some of the more delicate mathematical theory from the steady flow of examples in the main text. Besidesthe two new chapters, the second edition includes a more extensive list of exercises, many additions to the exposition of combinatorics, new material on rates of convergence to equilibrium in reversible Markov chains, a discussion ofbasic reproduction numbers in population modeling, and better coverage of Brownian motion. Because many chapters are nearly self-contained, mathematical scientist s from a variety of backgrounds will find Applied Probability useful as areference. Kenneth Lange is the Rosenfeld Professor of Computational Genetics in the Departments of Biomathematics and Human Gene
Note:Springer eBooks
Contents:Basic Notions of Probability Theory
Calculation of Expectations
Convexity, Optimization, and Inequalities
Combinatorics
Combinatorial Optimization
Poisson Processes
Discrete
Time Markov Chains
Continuous
Time Markov Chains
Branching Processes
Martingales
Diffusion Processes
Asymptotic Methods
Numerical Methods
Poisson Approximation
Number Theory
Appendix: Mathematical Review
ISBN:9781441971654
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X : v0
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer science , Computer simulation , Biology Mathematics , Computer science Mathematics , Distribution (Probability theory) , Mathematical statistics
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Call number:SPRINGER-2010-9781441969446:ONLINE Show nearby items on shelf
Title:Frontiers of Statistical Decision Making and Bayesian Analysis [electronic resource] : In Honor of James O. Berger
Author(s): Ming-Hui Chen
Peter Mller
Dongchu Sun
Keying Ye
Dipak K Dey
Date:2010
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This bookprovides a review of c urrent research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objectiveBayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods forcategorical and spatio-temporal data analysis and posterior simulation met hods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modelingand applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical andapplied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian stat istics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians whomake use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and re search scholars in statistics or biostatistics who wish to acquaint themselves with currentresearch frontiers. Ming-Hui Chen is Professor of Statistics at the University of Connecticut Dipak K. Dey is Head and Professor of Statistics at the University of Connecticut Peter Mller is Professor of Biostatistics at theUniversity of Texas M. D. Anderson Cancer Center Dongchu Sun is Professor of Statistics at the University of Missouri- Columbia and Keyi
Note:Springer eBooks
Contents:Objective Bayesian Inference with Applications
Bayesian Decision Based Estimation and Predictive Inference
Bayesian Model Selection and Hypothesis Tests
Bayesian Inference for Complex Computer Models
Bayesian Nonparametrics and Semi
parametrics
Bayesian Influence and Frequentist Interface
Bayesian Clinical Trials
Bayesian Methods for Genomics, Molecular and Systems Biology
Bayesian Data Mining and Machine Learning
Bayesian Inference in Political Science, Finance, and Marketing Research
Bayesian Categorical Data Analysis
Bayesian Geophysical, Spatial and Tempora
ISBN:9781441969446
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2010-9781441915726:ONLINE Show nearby items on shelf
Title:Statistical Methods for Disease Clustering [electronic resource]
Author(s): Toshiro Tango
Date:2010
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:The development of powerful computing environment and the geographical information system (GIS) in recent decades has thrust the analysis of geo-referenced disease incidence data into the mainstream of spatial epidemiology. Thisbook offers a modern p erspective on statistical methods for detecting disease clustering, an indispensable procedure to find a statistical evidence on aetiology of the disease under study. With increasing public health concerns aboutenvironmental risks, the need for sophistica ted methods for analyzing spatial health events is immediate. Furthermore, the research area of statistical methods for disease clustering now attracts a wide audience due to the perceivedneed to implement wide-ranging monitoring systems to detect possibl e health-related events such as the occurrence of the severe acute respiratory syndrome (SARS), pandemic influenza and bioterrorism As an invaluable resource for a widerange of audience including public health researchers, epidemiologists and biostatistia ns, this book features: A concise introduction to basic concepts of disease clustering/clusters A historical overview of methods for diseaseclustering A detailed treatment of selected methods useful for practical investigation of disease clustering Analys is and illustration of methods for a variety of real data sets Toshiro Tango, Ph.D., is the Director of Department ofTechnology Assessment and Biostatistics of National Institute of Public Health, Japan. He has published a number of methodological and app lied articles on various aspects of biostatistics. He is Past President of the Japanese Region ofthe International Biometric Society. He has served as Associate Editor for several journals including Statistics in Medicine and Biometrics
Note:Springer eBooks
Contents:Introduction
Clustering and clusters
Disease mapping: Visualization of spatial clustering
Tests for temporal clustering
General tests for spatial clustering: Regional count data
General tests for spatial clustering: Case
control point data
Tests for space
time clustering
Focused tests for spatial clustering
Space
time scan statistics
ISBN:9781441915726
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Oncology , Epidemiology , Biometrics , Statistical methods
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Call number:SPRINGER-2010-9781441904683:ONLINE Show nearby items on shelf
Title:Multivariate Nonparametric Methods with R [electronic resource] : An approach based on spatial signs and ranks
Author(s): Hannu Oja
Date:2010
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:This book offers a new, fairly efficient, and robust alternative to analyzing multivariate data. The analysis of data based on multivariate spatial signs and ranks proceeds very much as does a traditional multivariate analysisrelying on the assumptio n of multivariate normality the regular L2 norm is just replaced by different L1 norms, observation vectors are replaced by spatial signs and ranks, and so on. A unified methodology starting with the simpleone-sample multivariate location problem and proc eeding to the general multivariate multiple linear regression case is presented. Companion estimates and tests for scatter matrices are considered as well. The R package MNM is availablefor computation of the procedures. This monograph provides an up-to-d ate overview of the theory of multivariate nonparametric methods based on spatial signs and ranks. The classical book by Puri and Sen (1971) uses marginal signs andranks and different type of L1 norm. The book may serve as a textbook and a general referen ce for the latest developments in the area. Readers are assumed to have a good knowledge of basic statistical theory as well as matrix theory.Hannu Oja is an academy professor and a professor in biometry in the University of Tampere. He has authored and c oauthored numerous research articles in multivariate nonparametrical and robust methods as well as in biostatistics
Note:Springer eBooks
Contents:Introduction
Multivariate location and scatter models
Location and scatter functionals and sample statistics
Multivariate signs and ranks
One
sample problems: Hotellings T2
test
One
sample problem: Spatial sign test and spatial median
One
sample problem: Spatial signed
rank test and Hodges
Lehmann estimate
One
sample problem: Comparisons of tests and estimates
One
sample problem: Inference for shape
Multivariate tests of independence
Several
sample location problem
Randomized blocks
Multivariate linear regression
Analysis of cluster
correlated data
ISBN:9781441904683
Series:e-books
Series:SpringerLink (Online service)
Series:Lecture Notes in Statistics, 0930-0325 : v199
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer simulation , Biometrics , Computer science Mathematics , Mathematical statistics , Econometrics
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Call number:SPRINGER-2010-9780387927107:ONLINE Show nearby items on shelf
Title:Comparing Distributions [electronic resource]
Author(s): Olivier Thas
Date:2010
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
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Note:Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing. Whereas the latter includes only formal statistical hypothesis tests for the one-sample and the K-sampleproblems, this book pres ents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the nullhypothesis. Despite the historically seemingly diffe rent development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone. This book consists of two parts. In the first partstatistical methods for the one-sample problem are discussed. The second part of the book treats the K-sample problem. Many sections of this second part of the book may be of interest to every statistician who is involved incomparative studies. The book gives a self-contained theoretical treatment of a wide range of goodness-o f-fit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric,semiparametric and nonparametric theory, which is kept at an intermediate level the intuition and heuristics behind the methods a re usually provided as well. The book contains many data examples that are analysed with the cd R-packagethat is written by the author. All examples include the R-code. Because many methods described in this book belong to the basic toolbox of almost ever y statistician, the book should be of interest to a wide audience. In particular, thebook may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied statisticians may also be interested because of themany examples, the R-code and the stress on the informative nature of the procedures. Olivier Thas is Associate Professor of Biostatistics
Note:Springer eBooks
Contents:One
Sample Problems
Preliminaries (Building Blocks)
Graphical Tools
Smooth Tests
Methods Based on the Empirical Distribution Function
Two
Sample and K
Sample Problems
Preliminaries (Building Blocks)
Graphical Tools
Some Important Two
Sample Tests
Smooth Tests
Methods Based on the Empirical Distribution Function
Two Final Methods and Some Final Thoughts
ISBN:9780387927107
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Statistical methods , Operations research , Social sciences Methodology , Psychometrics
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Call number:SPRINGER-2010-9780387687650:ONLINE Show nearby items on shelf
Title:Introduction to Probability Simulation and Gibbs Sampling with R [electronic resource]
Author(s): Eric A Suess
Bruce E Trumbo
Date:2010
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
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Note:The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete andcontinuous states. Applica tions include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds ofgenetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate theuse of Gibbs samplers to find posterior distributions and interval estim ates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of itsinterface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experie nce using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems(along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields its large number of figures and its extraordinarilylarge number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are prov ided for many of the problems. These features make the book ideal for students ofstatistics at the senior undergraduate and at the beginning graduate levels. Eric A. Suess is Chair and Professor of Statistics and Biostatistics and Bruce E. Trumbo is Profe ssor Emeritus of Statistics and Mathematics, both atCalifornia State University, East Bay. Professor Suess is experienced in applications of Bayesian methods and Gibbs sampling to epidemiology. Profes
Note:Springer eBooks
Contents:Introductory Examples: Simulation, Estimation, and Graphics
Generating Random Numbers
Monte Carlo Integration and Limit Theorems
Sampling from Applied Probability Models
Screening Tests
Markov Chains with Two States
Examples of Markov Chains with Larger State Spaces
Introduction to Bayesian Estimation
Using Gibbs Samplers to Compute Bayesian Posterior Distributions
Using WinBUGS for Bayesian Estimation
Appendix: Getting Started with R
ISBN:9780387687650
Series:e-books
Series:SpringerLink (Online service)
Series:Use R : v0
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2010-9780387686363:ONLINE Show nearby items on shelf
Title:Design and Analysis of Vaccine Studies [electronic resource]
Author(s): M. Elizabeth Halloran
Jr. Longini Ira M
Claudio J Struchiner
Date:2010
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:Widespread immunization has many different kinds of effects in individuals and populations, including in the unvaccinated individuals. The challenge is in understanding and estimating all of these effects. This book presents aunified conceptual frame work of the different effects of vaccination at the individual and at the population level. The book covers many different vaccine effects, including vaccine efficacy for susceptibility, for disease, forpost-infection outcomes, and for infectiousness. The book includes methods for evaluating indirect, total and overall effects of vaccination programs in populations. Topics include household studies, evaluating correlates of immuneprotection, and applications of casual inference. Material on concepts of in fectious disease epidemiology, transmission models, casual inference, and vaccines provides background for the reader. This is the first book to presentvaccine evaluation in this comprehensive conceptual framework. This book is intended for colleagues and students in statistics, biostatistics, epidemiology, and infectious diseases. Most essential concepts are described in simplelanguage accessible to epidemiologists, followed by technical material accessible to statisticians. M. Elizabeth Halloran and Ira Longini are professors of biostatistics at the University of Washington and the Fred Hutchinson CancerResearch Center in Seattle. Claudio Struchiner is professor of epidemiology and biostatistics at the Brazilian School of Public Health of the Oswaldo Cr uz Foundation in Rio de Janeiro. The authors are prominent researchers in the area.Halloran and Struchiner developed the study designs for dependent happenings to delineate indirect, total, and overall effects. Halloran has made contributions at the inter face of epidemiological methods, causal inference, andtransmission dynamics. Longini works in the area of stochastic processes applied to epidemiological infectious disease problems, specializing in the
Note:Springer eBooks
Contents:Introduction and examples
Overview of vaccine effects and study designs
Immunology and early phase trials
binomial and stochastic transmission models
R0 and deterministic models
Evaluating protective effects of vaccination
Modes of action and time
varying VES
Further Evaluation of Protective Effects
Vaccine effects on post
infection outcomes
House
hold based studies
Analysis of households in communities
Analysis of independent households
Assessing Indirect, total and overall effects
Randomization and baseline transmission
Surrogates of protection
ISBN:9780387686363
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Emerging infectious diseases
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Call number:SPRINGER-2009-9780387924076:ONLINE Show nearby items on shelf
Title:A First Course in Bayesian Statistical Methods [electronic resource]
Author(s): Peter D Hoff
Date:2009
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advancedreaders to quickly grasp th e principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extendthe standard models to specialized data analysis s ituations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression,generalized linear mixed effects models, and semiparametric copula estimation. N umerous examples from the social, biological and physical sciences show how to implement these methodologies in practice. Monte Carlo summaries ofposterior distributions play an important role in Bayesian data analysis. The open-source R statistical compu ting environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statisticalmodels and example R-code is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book. PeterHoff is an Associate Professor of Statistics and Biostatistics at the University of Washington. He has developed a variety of Bayesian methods for mul tivariate data, including covariance and copula estimation, cluster analysis, mixturemodeling and social network analysis. He is on the editorial board of the Annals of Applied Statistics
Note:Springer eBooks
Contents:Introduction and examples
Belief, probability and exchangeability
One parameter models
Monte Carlo approximation
The normal model
Posterior approximation with the Gibbs sampler
The multivariate normal model
Group comparisons and hierarchical modeling
Linear regression
Nonconjugate priors and the Metropolis
Hastings algorithm
Linear and generalized linear mixed effects models
Latent variable methods for ordinal data
ISBN:9780387924076
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer science , Data mining , Mathematical statistics , Econometrics , Social sciences Methodology
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Call number:SPRINGER-2009-9780387096162:ONLINE Show nearby items on shelf
Title:Nonlinear Regression with R [electronic resource]
Author(s): Christian Ritz
Jens Carl Streibig
Date:2009
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevantfunctions, packages and documentation are scattered across the R environment. This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology,chemistry, engineering, medicine and toxico logy. The book begins with an introduction on how to fit nonlinear regression models in R. Subsequent chapters explain in more depth the salient features of the fitting function nls(), the useof model diagnostics, the remedies for various model departures , and how to do hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered.Christian Ritz has a PhD in biostatistics from the Royal Veterinary and Agricultural Unive rsity. For the last 5 years he has been working extensively with various applications of nonlinear regression in the life sciences and relateddisciplines, authoring several R packages and papers on this topic. He is currently doing postdoctoral research a t the University of Copenhagen. Jens C. Streibig is a professor in Weed Science at the University of Copenhagen. He hasfor more than 25 years worked on selectivity of herbicides and more recently on the ecotoxicology of pesticides and has extensive experi ence in applying nonlinear regression models. Together with the first author he has developed shortcourses on the subject of this book for students in the life sciences
Note:Springer eBooks
Contents:Introduction
Getting started
Starting values and self starters
More on nls ()
Model diagnostics
Remedies for model variations
Uncertainty, hypothesis testing and model selection
Grouped data
Appendix A: Datasets and models
Appendix B: Self starter functions
Appendix C: Packages and functions
References
Index
ISBN:9780387096162
Series:e-books
Series:SpringerLink (Online service)
Series:Use R
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Toxicology , Epidemiology , Forests and forestry , Mathematical statistics , Engineering
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Call number:SPRINGER-2008-9780387790541:ONLINE Show nearby items on shelf
Title:Introductory Statistics with R [electronic resource]
Author(s): Peter Dalgaard
Date:2008
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis andmethodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples withliberal commenting of the code and the output, from t he computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statisticalstandard distributions, one- and two-sample tests with continuous data, regressi on analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last sixchapters contain introductions to multiple linear regression analysis, linear models in general, logi stic regression, survival analysis, Poisson regression, and nonlinear regression. In the second edition, the text and code have beenupdated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The in troductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answersare now provided in an Appendix. Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of HealthSciences. He has been a member of the R Core Team since 1997
Note:Springer eBooks
ISBN:9780387790541
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics and Computing, 1431-8784
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Bioinformatics , Biology Data processing , Mathematical statistics
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Call number:SPRINGER-2008-9780387781679:ONLINE Show nearby items on shelf
Title:Statistical Methods for Environmental Epidemiology with R [electronic resource] : A Case Study in Air Pollution and Health
Author(s): Francesca Dominici
Roger D Peng
Date:2008
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:Advances in statistical methodology and computing have played an important role in allowing researchers to more accurately assess the health effects of ambient air pollution. The methods and software developed in this area areapplicable to a wide arr ay of problems in environmental epidemiology. This book provides an overview of the methods used for investigating the health effects of air pollution and gives examples and case studies in R which demonstratethe application of those methods to real data. The book will be useful to statisticians, epidemiologists, and graduate students working in the area of air pollution and health and others analyzing similar data. The authors describe thedifferent existing approaches to statistical modeling and cover ba sic aspects of analyzing and understanding air pollution and health data. The case studies in each chapter demonstrate how to use R to apply and interpret differentstatistical models and to explore the effects of potential confounding factors. A working k nowledge of R and regression modeling is assumed. In-depth knowledge of R programming is not required to understand and run the examples.Researchers in this area will find the book useful as a ``live'' reference. Software for all of the analyses in the bo ok is downloadable from the web and is available under a Free Software license. The reader is free to run theexamples in the book and modify the code to suit their needs. In addition to providing the software for developing the statistical models, the aut hors provide the entire database from the National Morbidity Mortality and Air PollutionStudy (NMMAPS) in a convenient R package. With the database, readers can run the examples and experiment with their own methods and ideas. Roger D. Peng is an Assistan t Professor in the Department of Biostatistics at the Johns HopkinsBloomberg School of Public Health. He is a prominent researcher in the areas of air pollution and health risk assessment and statistica
Note:Springer eBooks
Contents:Overview of the data
Overview of statistical modeling
Statistical issues related to air pollution and health
Summarizing evidence of the health effects of air pollution
Case studies
ISBN:9780387781679
Series:e-books
Series:SpringerLink (Online service)
Series:Use R
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2008-9780387749785:ONLINE Show nearby items on shelf
Title:Introduction to Empirical Processes and Semiparametric Inference [electronic resource]
Author(s): Michael R Kosorok
Date:2008
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inferencefor complex models an d in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learningabout and doing research in empirical pro cesses and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level ofthe book is suitable for a second year graduate course in sta tistics or biostatistics, provided the students have had a year of graduate level mathematical statistics and a semester of probability. The book consists of three parts. Thefirst part is a concise overview of all of the main concepts covered in the book with a minimum of technicalities. The second and third parts cover the two respective main topics of empirical processes and semiparametric inference indepth. The connections between these two topics is also demonstrated and emphasized throughout the text . Each part has a final chapter with several case studies that use concrete examples to illustrate the concepts developed so far.The last two parts also each include a chapter which covers the needed mathematical preliminaries. Each main idea is introduce d with a non-technical motivation, and examples are given throughout to illustrate important concepts.Homework problems are also included at the end of each chapter to help the reader gain additional insights. Michael R. Kosorok is Professor and Chair, De partment of Biostatistics, and Professor, Department of Statistics and OperationsResearch, at the University of North Carolina at Chapel Hill. His research has focused on the application of empirical pr
Note:Springer eBooks
Contents:Introduction
An Overview of The Empirical Processes
Overview of Semiparametric Inference
Case Studies I
Introduction to Empirical Processes
Preliminiaries for Empirical Processes
Stochastic Convergence
Empirical Process Methods
Entropy Calculations
Bootstrapping Empirical Processes
Additional Empirical Process Results
The Functional Delta Method
Z
Estimators
M
Estimators
Case Studies II
Introduction To Semiparametric Inference
Seimparametric Models and Efficiency
Efficient Inference for Fininte
Dimensional Parameters
Efficient Inference for
ISBN:9780387749785
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2008-9780387685601:ONLINE Show nearby items on shelf
Title:Survival and Event History Analysis [electronic resource] : A Process Point of View
Author(s): Odd O Aalen
rnulf Borgan
Hkon K Gjessing
Date:2008
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
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Note:Time-to-event data are ubiquitous in fields such as medicine, biology, demography, sociology, economics and reliability theory. Recently, a need to analyze more complex event histories has emerged. Examples are individuals thatmove among several stat es, frailty that makes some units fail before others, internal time-dependent covariates, and the estimation of causal effects from observational data. The aim of this book is to bridge the gap between standardtextbook models and a range of models where t he dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochasticintegrals fit very nicely with censored data. Beginning with standa rd analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes arealso used as natural models for individual frailty they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors showhow dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspec t seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, aredeveloped in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigator s who use event history methods and want a better understanding of the statistical concepts. It issuitable as a textbook for graduate courses in statistics and biostatistics. Odd O. Aalen is professor of medical statistics at the University of Oslo, Norwa y. His Ph.D. from the University of California, Berkeley in 1975 introducedcounting processes and martingales in event history analysis. He has also contributed to numerous other areas of event history
Note:Springer eBooks
Contents:An introduction to survival and event history analysis
Stochastic processes in event history analysis
Nonparametric analysis of survival and event history data
Regression models
Parametric counting process models
Unobserved heterogeneity: The odd effects of frailty
Multivariate frailty models
Marginal and dynamic models for recurrent events and clustered survival data
Causality
First passage time models: Understanding the shape of the hazard rate
Diffusion and Lvy process models for dynamic frailty
ISBN:9780387685601
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Epidemiology , Distribution (Probability theory) , System safety , Econometrics
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Call number:SPRINGER-2007-9783540326915:ONLINE Show nearby items on shelf
Title:Statistical Methods for Biostatistics and Related Fields [electronic resource]
Author(s): Wolfgang Hrdle
Yuichi Mori
Philippe Vieu
Date:2007
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
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Note:Biostatistics is one of the scientific fields for which the recent developments have been extremely important. It is also strongly related to other scientific disciplines involving statistical methodology. The aim of this book isto cover a wide scope of recent statistical methods used by scientists in biostatistics as well as in other related fields such as chemometrics, environmetrics and geophysics. The contributed papers, coming from internationallyrecognized researchers, present various statistic al methodologies together with a selected scope of their main mathematical properties and their applications in real case studies, making this book of interest to a wide audience amongresearchers and students in statistics. Each method is accompanied with interactive and automatic Xplore routines, available on-line, allowing people to reproduce the proposed examples or to apply the methods to their own realdatasets. Thus this book will also be of special interest to practitioners
Note:Springer eBooks
Contents:Biostatistics
Discriminant Analysis Based on Continuous and Discrete Variables
Longitudinal Data Analysis with Linear Regression
A Kernel Method Used for the Analysis of Replicated Micro
array Experiments
Kernel Estimates of Hazard Functions for Biomedical Data Sets
Partially Linear Models
Analysis of Contingency Tables
Identifying Coexpressed Genes
Bootstrap Methods for Testing Interactions in GAMs
Survival Trees
A Semiparametric Approach to Estimate Reference Curves for Biophysical Properties of the Skin
Survival Analysis
Related Sciences
Ozone Pollutio
ISBN:9783540326915
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2007-9780387735085:ONLINE Show nearby items on shelf
Title:Multivariate Statistics [electronic resource] : Exercises and Solutions
Author(s): Wolfgang Härdle
Zdeněk Hlávka
Date:2007
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables andpresents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus andbasic multivariate methods in real life situatio ns. It contains altogether 234 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available inthe R or XploRe languages. The corresponding libraries are downloadable from the Springer link web pages and from the authors home pages. Wolfgang Hrdle is Professor of Statistics at Humboldt-Universitt zu Berlin. He studiedmathematics, computer science and physics at the University of Karlsruhe and received his Dr.rer.nat. at the University of Heidelberg. Later he had positions at Frankfurt and Bonn before he became professeur ordinaire at UniversitCatholique de Louvain. His current research topic is modelling of implied volatilities and the quantitative analysis of financi al markets. Zdenek Hlvka studied mathematics at the Charles University in Prague and biostatistics atLimburgs Universitair Centrum in Diepenbeek. Later he held a position at Humboldt-Universitt zu Berlin before he became a member of the Department of Prob ability and Mathematical Statistics at Charles University in Prague
Note:Springer eBooks
Contents:Comparison of Batches
A Short Excursion Into Matrix Algebra
Moving to Higher Dimensions
Multivariate Distributions
Theory of The Multinormal
Theory of Estimation
Hypothesis Testing
Decomposition of Data Matrices by Factors
Principal Components Analysis
Factor Analysis
Cluster Analysis
Discriminate Analysis
Correspondence Analysis
Canonical Correlation Analysis
Multidimensional Scaling
Conjoint Measurement Analysis
Applications in Finance
Highly Interactive, Computationally Intensive Techniques
ISBN:9780387735085
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Computer science Mathematics , Visualization , Mathematical statistics
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Call number:SPRINGER-2007-9780387681542:ONLINE Show nearby items on shelf
Title:Statistical Genetics of Quantitative Traits [electronic resource] : Linkage, Maps, and QTL
Author(s): Rongling Wu
George Casella
Chang-Xing Ma
Date:2007
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:Springer 2013 e-book collections
Note:The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. Itconcentrates on the li nkage analysis of markers, map construction and quantitative trait locus (QTL) mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in theconstruction of general models and algorithms fo r linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlifespecies. The book includes a detailed description of each approach and the step-by-step demonstration of live-example analyses designed to explain the utilization and usefulness of statistical methods. The book also includes exercisesets and computer codes for all the analyses used. This book can serve as a textbook for gra duates and senior undergraduates in genetics, agronomy, forest biology, plant breeding and animal sciences. It will also be useful toresearchers and other professionals in the areas of statistics, biology and agriculture. Rongling Wu is Associate Professo r of Statistics at the University of Florida, Gainesville. He currently serves as Associate Editor for sixgenetics and bioinformatics journals. Chang-Xing Ma is Assistant Professor of Biostatistics at the State University of New York at Buffalo. George Ca sella is Distinguished Professor of Statistics and Distinguished Member of theGenetics Institute at the Univesity of Florida, Gainesville. He is a fellow of the American Statistical Association and the Institute of Mathematical Sciences, and the author of four other statistics books
Note:Springer eBooks
Contents:Basic Genetics
Basic Statistics
Linkage Analysis and Map Construction
A General Model for Linkage Analysis in Controlled Crosses
Linkage Analysis with Recombinant Inbred Lines
Linkage Analysis for Distorted and Misclassified Markers
Special Considerations in Linkage Analysis
The Structure of QTL Mapping
Marker Analysis of Phenotypes
Regression
Based Interval Mapping
Maximum Likelihood Interval Mapping
Threshold and Precision Analysis
Composite QTL Mapping
QTL Mapping in Outbred Pedigrees
ISBN:9780387681542
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Life sciences , Biometrics , Bioinformatics , Plant breeding , Animal genetics , Genetics Mathematics , Statistics
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Call number:SPRINGER-2006-9783790817096:ONLINE Show nearby items on shelf
Title:Compstat 2006 - Proceedings in Computational Statistics [electronic resource] : 17th Symposium Held in Rome, Italy, 2006
Author(s): Alfredo Rizzi
Maurizio Vichi
Date:2006
Publisher:Heidelberg : Physica-Verlag HD
Size:1 online resource
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Note:Springer 2013 e-book collections
Note:The book provides new developments in data analysis and statistical multivariate methods, computational statistics and algorithms, including new topics which are of central interest to modern statistics. The reader will findadvanced methodologies and computational methods which are very helpful to analyze real phenomena characterized by large data bases. Furthermore, the volume includes papers devoted to original and innovative applications of recentstatistical theory and complex approaches of statis tical data analysis
Note:Springer eBooks
Contents:Part I: Classification and Clustering
Part II: Image Analysis and Signal Processing
Part III: Data Visualization
Part IV: Multivariate Analysis
Part V: Web Based Teaching
Part VI: Algorithms
Part VII: Robustness
Part on CD: Part VIII: Categorical Data Analysis
Part IX: Multivariate Data Analysis II
Part X: Classification and Clustering II
Part XI: Data Mining
Part XII: Biostatistics
Part XIII: Resampling Methods
Part XIV Functional Data Analysis
Part XV: Time Series Analysis and Spatial Analysis
Part XVI: Nonparametric Statistics and Smoothing
Pa
ISBN:9783790817096
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer science , Information storage and retrieval systems , Mathematical statistics
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Call number:SPRINGER-2006-9783540313144:ONLINE Show nearby items on shelf
Title:From Data and Information Analysis to Knowledge Engineering [electronic resource] : Proceedings of the 29th Annual Conference of the Gesellschaft fr Klassifikation e.V. University of Magdeburg, March 911, 2005/ edited by Myra Spiliopoulou, Rudolf Kr use, Christian Borgelt, Andreas Nrnberger, Wolfgang Gaul
Author(s): Myra Spiliopoulou
Rudolf Kruse
Christian Borgelt
Andreas Nrnberger
Wolfgang Gaul
Date:2006
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
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Note:Springer 2013 e-book collections
Note:The volume contains revised versions of selected papers presented during the 29th Annual Conference of the Gesellschaft fr Klassifikation (GfKl), the German Classification Society, held at the Otto-von-Guericke-University ofMagdeburg, Germany, in Mar ch 2005. In addition to papers on the traditional subjects Classification, Clustering, and Data Analysis, there are many papers on a wide range of topics with a strong relation to Computer Science. Examplesare Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization. Application-oriented topics include Economics, Marketing, Banking and Finance, Medicine, Bioinformatics, Biostatistics,and Music Analysis
Note:Springer eBooks
Contents:Plenaries and Semi
plenaries
Clustering
Discriminant Analysis
Classification with Latent Variable Models
Multiway Classification and Data Analysis
Ranking, Multi
label Classification, Preferences
PLS Path Modeling, PLS Regression and Classification
Robust Methods in Multivariate Statistics
Data Mining and Explorative Multivariate Data Analysis
Text Mining
Fuzzy Data Analysis
Economics and Mining in Business Processes
Banking and Finance
Marketing
Adaptivity and Personalization
User and Data Authentication in IT Security
Bioinformatics and Biostat
ISBN:9783540313144
Series:e-books
Series:SpringerLink (Online service)
Series:Studies in Classification, Data Analysis, and Knowledge Organization, 1431-8814
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Optical pattern recognition , Economics Statistics , Economics, Mathematical
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Call number:SPRINGER-2006-9780387462127:ONLINE Show nearby items on shelf
Title:Statistical Reasoning in Medicine [electronic resource] : The Intuitive P-Value Primer
Author(s): Lemuel A Moy
Date:2006
Edition:Second Edition
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
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Note:Lowers the Learning Curve for Physicians and Researchers! The successful Statistical Reasoning in Medicine: The Intuitive P-value Primer, with its novel emphasis on patient and community protection, illustrated the correct use ofstatistics in health care research for healthcare workers. Through clear explanations and examples, this book provided the non-mathematician with a foundation for understanding the underlying statistical reasoning process in clinicalresearch, the core principles of research d esign, and the correct use of statistical inference and p-values. The P-Value Primer 2nd Edition levels the learning curve of statistics for health care researchers by further de-emphasizingmathematical and computational devices, bringing the principles o f statistical reasoning closer to the uninitiated. Adding to the updated discussions of research design, hypothesis testing, regression analysis, and Bayes procedures,are new discussions of absolute and relative risk, as well as a lucid description of the number needed to treat (NNT). The multiple analysis issue is clearly defined, and a new description of the correct use and interpretation ofcombined endpoints in health care research is offered in an easily digestible format. The P-value Primer 2nd Editi on demolishes other obstacles that have impeded a clear understanding of the application of statistics in medicine. Theintertwined roles of epidemiology and biostatistics are depicted. In addition to a description of the non-technical history of statistic s, a new discussion describes the active cultural forces that have historically argued against theuse of probability and statistics, placing the current applications and controversies involving p-values in context. New illustrations of the difficulties ph ysicians and health care providers face in research are offered, and thedifferences between research skills and statistical skills are distinguished. New discussion describing the process of scientific
Note:Springer eBooks
Contents:Prologue
The Basis of Statistical Reasoning in Medicine
Search Versus Research
A Hypothesis
Testing Primer
Mistaken Identity: P
values in Epidemiology
Shrine Worship
P
values, Power, and Efficacy
Scientific Reasoning, P
values, and the Court
One
Sided Versus Two
Sided Testing
Multiple Testing and Combined Endpoints
Subgroup Analyses
P
values and Regression Analyses
Bayesian Analysis: Posterior P
values
ISBN:9780387462127
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Epidemiology
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Call number:SPRINGER-2006-9780387449708:ONLINE Show nearby items on shelf
Title:Statistical Monitoring of Clinical Trials [electronic resource] : A Unified Approach
Author(s): Michael A Proschan
K. K. Gordan Lan
Janet Turk Wittes
Date:2006
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
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Note:The approach taken in this book is, to studies monitored over time, what the Central Limit Theorem is to studies with only one analysis. Just as the Central Limit Theorem shows that test statistics involving very different typesof clinical trial outc omes are asymptotically normal, this book shows that the joint distribution of the test statistics at different analysis times is asymptotically multivariate normal with the correlation structure of Brownianmotion (``the B-value) irrespective of the test statistic. The so-called B-value approach to monitoring allows us to use, for different types of trials, the same boundaries and the same simple formula for computing conditional power.Although Brownian motion may sound complicated, the authors make the a pproach easy by starting with a simple example and building on it, one piece at a time, ultimately showing that Brownian motion works for many different types ofclinical trials. The book will be very valuable to statisticians involved in clinical trials. The main body of the chapters is accessible to anyone with knowledge of a standard mathematical statistics text. More mathematically advancedreaders will find rigorous developments in appendices at the end of chapters. Reading the book will develop insigh t into not only monitoring, but power, survival analysis, safety, and other statistical issues germane to clinicaltrials. Michael Proschan, Gordon Lan, and Janet Wittes are elected Fellows of the American Statistical Association. All have spent formative years in the Biostatistics Research Branch of the National Heart, Lung, and Blood Institute(NHLBI/NIH). While there, they were intimately involved in the design and statistical monitoring of large-scale randomized clinical trials, developing methodology t o aid in their monitoring. For example, Lan developed, with DeMets, thenow widely-used spending function approach to group sequential designs, whose properties were further investigated by Proschan. Th
Note:Springer eBooks
Contents:A General Framework
Power: Conditional, Unconditional, and Predictive
Historical Monitoring Boundaries
Spending Functions
Practical Survival Monitoring
Inference Following a Group
Sequential Trial
Options When Brownian Motion Does Not Hold
Monitoring for Safety
Bayesian Monitoring
Adaptive Sample Size Methods
Topics Not Covered
Appendix I: The Logrank and Related Tests
Appendix II: Group
Sequential Software
ISBN:9780387449708
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2006-9780387301075:ONLINE Show nearby items on shelf
Title:Data Monitoring in Clinical Trials [electronic resource] : A Case Studies Approach
Author(s): David L DeMets
Curt D Furberg
Lawrence M Friedman
Date:2006
Publisher:New York, NY : Springer US
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Randomized clinical trials are the gold standard for establishing many clinical practice guidelines and are central to evidence based medicine. Obtaining the best evidence through clinical trials must be done within the boundariesof rigorous science and ethical principles. One fundamental principle is that trials should not continue longer than necessary to reach their objectives. Therefore, trials must be monitored for recruitment progress, quality of data,adherence to patient care or prevention sta ndards, and early evidence of benefit or harm. Frequently, a group of external experts, independent from the investigators and trial sponsor, is charged with this monitoring responsibility,especially for safety and early benefit. This group is referred to by various names, such as a data monitoring committee or a data and safety monitoring board. This book, through a series of case studies presented by many distinguishedclinical trial experts, illustrates the complexity of this monitoring process. The edi tors provide an overview of the process and a summary of a multitude of the lessons learned from the cases presented. This book should be useful toanyone serving on a data and safety monitoring board, or planning to do so, for colleagues in academia, indu stry and governmental agencies, and for teaching students in biostatistics, epidemiology, clinical trials and medical ethics.No other text has as extensive a collection of cases which provide insight into the many issues, often conflicting, that must be e xamined before recommendations to continue or discontinue a trial can be made. While depth in statisticalmethods is not required, some familiarity with statistical design and analysis issues in clinical trials is helpful. The cases cover trials which were terminated early for convincing evidence of benefit, or for harmful effects. Caseswith complex issues are also included. This series of cases should provide broad background information for potential m
Note:Springer eBooks
Contents:From the contents: Assessing Possible Late Treatment Effects Early
The Diabetic Retinopathy Study Experience
Data and Safety Monitoring in the Beta
Blocker Heart Attack Trial
Early Experiences in Formal Monitoring Methods
Data Monitoring for the Aspirin Component of the Physicians Health Study
Importance of Secondary Outcomes
Early Termination of the Stroke Prevention in Atrial Fibrillation I Trial
Protecting Participant Interests in the Face of Scientific Uncertainties and the Cruel Play of Chance
Early Termination of the Diabetes Control and Complications Trial Methodolog
ISBN:9780387301075
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2006-9780387277820:ONLINE Show nearby items on shelf
Title:Statistical Monitoring of Clinical Trials [electronic resource] : Fundamentals for Investigators
Author(s): Lemuel A Moy
Date:2006
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:Statistical Monitoring of Clinical Trials: Fundamentals for Investigators introduces the investigator and statistician to monitoring procedures in clinical research. Clearly presenting the necessary background with limited use ofmathematics, this boo k increases the knowledge, experience, and intuition of investigations in the use of these important procedures now required by the many clinical research efforts. The author provides motivated clinicalinvestigators the background, correct use, and interp retation of these monitoring procedures at an elementary statistical level. He defines terms commonly used such as group sequential procedures and stochastic curtailment innon-mathematical language and discusses the commonly used procedures of Pocock, OBr ienFleming, and LanDeMets. He discusses the notions of conditional power, monitoring for safety and futility, and monitoring multiple endpointsin the study. The use of monitoring clinical trials is introduced in the context of the evolution of clinical re search and one chapter is devoted to the more recent Bayesian procedures. Dr. Lemuel A. Moy, M.D., Ph.D. is a physicianand a biostatistician at the University of Texas School of Public Health. He is a diplomat of the National Board of Medical Examiners an d is currently Professor of Biostatistics at the University of Texas School of Public Health inHouston where he holds a full time faculty position. Dr. Moy has carried out cardiovascular research for twenty years and continues to be involved in the design , execution and analysis of clinical trials, both reporting to andserving on many Data Monitoring Committees. He has served in several clinical trials sponsored by both the U.S. government and private industry. In addition, Dr. Moy has served as statistic ian/epidemiologist for six years on both theCardiovascular and Renal Drug Advisory Committee to the Food and Drug Administration and the Pharmacy Sciences Advisory Committee to the FDA. H
Note:Springer eBooks
Contents:Here, there be dragons
The basis of statistical reasoning in medicine
Probability tools and stopping rules
Issues and intuitions in path analysis
Group sequential analysis procedures
Looking forward: conditional power
Safety and futility
Bayesian statistical monitoring
ISBN:9780387277820
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Neurosciences , Epidemiology
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Call number:SPRINGER-2005-9781846281242:ONLINE Show nearby items on shelf
Title:An R and S-PLUS Companion to Multivariate Analysis [electronic resource]
Author(s): Brian Sidney Everitt
Date:2005
Publisher:London : Springer London : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results. This requires the use of one or other of the manymethods of multivariate anal ysis, and the use of a suitable software package such as S-PLUS or R. In this book the core multivariate methodology is covered along with some basic theory for each method described. The necessary R andS-PLUS code is given for each analysis in the book, with any differences between the two highlighted. A website with all the datasets and code used in the book can be found at http://biostatistics.iop.kcl.ac.uk/publications/everitt/.Graduate students, and advanced undergraduates on applied statistics cours es, especially those in the social sciences, will find this book invaluable in their work, and it will also be useful to researchers outside of statistics whoneed to deal with the complexities of multivariate data in their work. Brian Everitt is Emeritus Professor of Statistics, Kings College, London
Note:Springer eBooks
Contents:Multivariate Data and Multivariate Analysis
Looking at Multivariate Data
Principal Components Analysis
Exploratory Factor Analysis
Multidimensional Scaling and Correspondence Analysis
Cluster Analysis
Grouped Multivariate Data: Multivariate Analysis of Variance and Discriminant Function Analysis
Multiple Regression and Canonical Correlation
The Analysis of Repeated Measures Data
Appendix
ISBN:9781846281242
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2005-9780817644222:ONLINE Show nearby items on shelf
Title:Advances in Ranking and Selection, Multiple Comparisons, and Reliability [electronic resource] : Methodology and Applications
Author(s): N Balakrishnan
H. N Nagaraja
N Kannan
Date:2005
Publisher:Boston, MA : Birkhuser Boston
Size:1 online resource
Note:Springer e-book platform
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Note:S. Panchapakesan has made significant contributions to ranking and selection and has published in many other areas of statistics, including order statistics, reliability theory, stochastic inequalities, and inference. Written inhis honor, the twenty invited articles in this volume reflect recent advances in these fields and form a tribute to Panchapakesans influence and impact on these areas. Thematically organized, the chapters cover a broad range oftopics from: * Inference * Ranking and Selection * Multiple Comparisons and Tests * Agreement Assessment * Reliability * Biostatistics Featuring theory, methods, applications, and extensive bibliographies with special emphasis onrecent literature, this comprehensive reference work will serve researchers, practitioners, and graduate students in the statistical and applied mathematics communities
Note:Springer eBooks
Contents:From the contents: Preface
S. Panchapakesan Life and Works
Contributors
List or Tables
List of Figures
Part I: Inference
Score Test: Historical Review and Recent Developments
EM Algorithm and Optimal Censoring Schemes for Progressively Type
II Censored Bivariate Normal Data
Inference Guided Data Exploration
Discriminating Between Normal and Laplace Distributions
A Simple Classification Rule for Directional Data
Part II: Ranking and Selection
On Some Ranking and Selection Procedures for MANOVA Models with Applications
A Restricted Subset Selection Ru
ISBN:9780817644222
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Industry and Technology
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Distribution (Probability theory) , Mathematical statistics , Economics Statistics
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Call number:SPRINGER-2005-9780387293622:ONLINE Show nearby items on shelf
Title:Bioinformatics and Computational Biology Solutions Using R and Bioconductor [electronic resource]
Author(s): Robert Gentleman
Vincent J Carey
Wolfgang Huber
Rafael A Irizarry
Sandrine Dudoit
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted inthe open source statis tical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data frommicroarray, proteomic, and flow cytometry platfo rms curation and delivery of biological metadata for use in statistical modeling and interpretation statistical analysis of high-throughput data, including machine learning andvisualization, modeling and visualization of graphs and networks. The developer s of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data,and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text it is adynamic document. Code underlying all of the computations that are shown is made available on a companion website, and re aders can reproduce every number, figure, and table on their own computers. Robert Gentleman is Head of theProgram in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle. He is one of the two authors of the original R system and a leading member of the R core team. Vincent Carey is Associate Professor of Medicine(Biostatistics), Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School. Gentleman and Carey are co-founders of the Bioconductor project. Wolfgang Hub er is Group Leader in the European Molecular Biology Laboratory atthe European Bioinformatics Institute in Cambridge. He has made influential contributions to the error modeling of microarray data. Raf
Note:Springer eBooks
Contents:Preprocessing data from genomic experiments
Preprocessing Overview
Preprocessing High
density Oligonucleotide Arrays
Quality Assessment of Affymetrix GeneChip Data
Preprocessing Two
Color Spotted Arrays
Cell
Based Assays
SELDI
TOF Mass Spectrometry Protein Data
Meta
data: biological annotation and visualization
Meta
data Resources and Tools in Bioconductor
Querying On
line Resources
Interactive Outputs
Visualizing Data
Statistical analysis for genomic experiments
Analysis Overview
Distance Measures in DNA Microarray Data Analysis
Cluster Analysis of
ISBN:9780387293622
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Bioinformatics , Animal genetics
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Call number:SPRINGER-2005-9780387289809:ONLINE Show nearby items on shelf
Title:Models for Discrete Longitudinal Data [electronic resource]
Author(s): Geert Molenberghs
Geert Verbeke
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
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Note:This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector.This allows model fitti ng to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, includinggeneralized estimating equations and pseudo-like lihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular thegeneralized linear mixed model. Several frequently used procedures for mo del fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, suchas models for multivariate longitudinal measurements, random-effects models with serial corre lation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encounteredissue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis onsoftware, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the read er can skip the software-oriented chapters and sections without breaking the logical flow. GeertMolenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis ofnon-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (20012004) and as Associate Ed
Note:Springer eBooks
Contents:Introduction
Motivating Studies
Generalized Linear Models
Linear Mixed Models for Gaussian Longitudinal Data
Model Families
The Strength of Marginal Models
Likelihood
based Models
Generalized Estimating Equations
Pseudo
likelihood
Fitting Marginal Models with SAS
Conditional Models
Pseudo
likehood
From Subject
Specific to Random
Effects Models
Generalized Linear Mixed Models (GLMM)
Fitting Generalized Linear Mixed Models with SAS
Marginal Versus Random
Effects Models
Ordinal Data
The Epilepsy Data
Non
linear Models
Psuedo
likelihood for
ISBN:9780387289809
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2005-9780387283142:ONLINE Show nearby items on shelf
Title:Modeling Longitudinal Data [electronic resource]
Author(s): Robert E Weiss
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
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Note:Springer 2013 e-book collections
Note:Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the artand statistical scie nce of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyzes the time trend andcovariates, models the covariance matrix, and t hen draws conclusions. Covariance models covered include random effects, autoregressive, autoregressive moving average, antedependence, factor analytic, and completely unstructured modelsamong others. Longer expositions explore: an introduction to and cri tique of simple non-longitudinal analyses of longitudinal data, missing data concepts, diagnostics, and simultaneous modeling of two longitudinal variables.Applications and issues for random effects models cover estimation, shrinkage, clustered data, mode ls for binary and count data and residuals and residual plots. Shorter sections include a general discussion of how computationalalgorithms work, handling transformed data, and basic design issues. This book requires a solid regression course as backgroun d and is particularly intended for the final year of a Biostatistics or Statistics Masters degree curriculum.The mathematical prerequisite is generally low, mainly assuming familiarity with regression analysis in matrix form. Doctoral students in Biostati stics or Statistics, applied researchers and quantitative doctoral students indisciplines such as Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education will find this book invaluable. The book has many fig ures and tables illustrating longitudinal data andnumerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material
Note:Springer eBooks
Contents:to Longitudinal Data
Plots
Simple Analyses
Critiques of Simple Analyses
The Multivariate Normal Linear Model
Tools and Concepts
Specifying Covariates
Modeling the Covariance Matrix
Random Effects Models
Residuals and Case Diagnostics
Discrete Longitudinal Data
Missing Data
Analyzing Two Longitudinal Variables
Further Reading
ISBN:9780387283142
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics , Economics Statistics
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Call number:SPRINGER-2005-9780387272559:ONLINE Show nearby items on shelf
Title:Regression Methods in Biostatistics [electronic resource] : Linear, Logistic, Survival, and Repeated Measures Models
Author(s): Eric Vittinghoff
Stephen C Shiboski
David V Glidden
Charles E McCulloch
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Coxmodel for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they havein common. The authors point out the many-shar ed elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding,mediation, and interaction of causal effects in essentially the same way . The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics isassumed, a chapter reviewing basic statistical methods is included. Some advanced topics are cov ered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further readingare provided. For many students and researchers learning to use these methods, this one book may be all they need to c onduct and interpret multipredictor regression analyses. The authors are on the faculty in the Division ofBiostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences.The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Var iance Components (1992)
Note:Springer eBooks
Contents:Introduction
Exploratory and Descriptive Methods
Basic Statistical Methods
Linear Regression
Predictor Selection
Logistic Regression
Survival Analysis
Repeated Measures Analysis
Generalized Linear Models
Complex Surveys
Summary
ISBN:9780387272559
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Epidemiology
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Call number:SPRINGER-2005-9780387270807:ONLINE Show nearby items on shelf
Title:The Evaluation of Surrogate Endpoints [electronic resource]
Author(s): Tomasz Burzykowski
Geert Molenberghs
Marc Buyse
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Both humanitarian and commercial considerations have spurred intensive search for methods to reduce the time and cost required to develop new therapies. The identification and use of surrogate endpoints, i.e., measures that canreplace or supplement o ther endpoints in evaluations of experimental treatments or other interventions, is a general strategy that has stimulated both enthusiasm and skepticism. Surrogate endpoints are useful when they can be measuredearlier, more conveniently, or more frequent ly than the true endpoints of primary interest. Regulatory agencies around the globe, particularly in the United States, Europe, and Japan, are introducing provisions and policiesrelating to the use of surrogate endpoints in registration studies. But how can one establish the adequacy of a surrogate? What kind of evidence is needed, and what statistical methods portray that evidence most appropriately? Thisbook offers a balanced account on this controversial topic. The text presents major developments of the last couple of decades, together with a unified, meta-analytic framework within which surrogates can be evaluated from severalangles. Methodological development is coupled with perspectives on various therapeutic areas. Academic views are juxtaposed w ith standpoints of scientists working in the biopharmaceutical industry as well as of colleagues from theregulatory authorities. Tomasz Burzykowski is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Burzykowski ha s published methodological work on the analysis of survey data, meta-analyses ofclinical trials, and validation of surrogate endpoints. He is a co-author of numerous papers applying statistical methods to clinical data in different disease areas (cancer, cardiovascular diseases, dermatology, orthodontics). GeertMolenberghs is Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Molenberghs published methodological work on su
Note:Springer eBooks
Contents:Setting the Scene
Regulatory Aspects in Using Surrogate Markers in Clinical Trials
Notation and Motivating Studies
The History of Surrogate Endpoint Validation
Validation Using Single
trial Data: Mixed Binary and Continuous Outcomes
A Meta
analytic Validation Framework for Continuous Outcomes
The Choice of Units
Extensions of the Meta
analytic Approach to Surrogate Endpoints
Meta
analytic Validation with Binary Outcomes
Validation in the Case of Two Failure
time Endpoints
An Ordinal Surrogate for a Survival True Endpoint
A Combination of Longitudinal and Surviv
ISBN:9780387270807
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics
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Call number:SPRINGER-2004-9781441990761:ONLINE Show nearby items on shelf
Title:Proceedings of the Second Seattle Symposium in Biostatistics Analysis of Correlated Data
Author(s):
Date:2004
Size:1 online resource (331 p.)
Note:10.1007/978-1-4419-9076-1
Contents:Whither PQL? -- Correlation and marginal longitudinal kernel nonparametric regression -- Analysis of multivariate monotone missing data by a pseudolikelihood method -- Quantile regression for correlated observations -- Small sample
inference for clustered data -- Some appications of indirect inference to longitudinal and repeated events data -- On characterizing joint survivor functions by minima -- Nonparametric estimation of the bivariate survivor function -- A
semiparametric regression model for panel count data: When do pseudo-likelihood estimators become badly inefficient? -- Some biases that may affect kin-cohort studies for estimating the risks from indentified disease genes -- Optimal
structural nested models for optimal sequential decisions -- Addresses for Contact Authors -- List of Referees
ISBN:9781441990761
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Lecture Notes in Statistics: 179
Keywords: Statistics , Statistics , Statistics for Life Sciences, Medicine, Health Sciences
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Call number:SPRINGER-1997-9781468463163:ONLINE Show nearby items on shelf
Title:Proceedings of the First Seattle Symposium in Biostatistics Survival Analysis
Author(s):
Date:1997
Size:1 online resource (309 p.)
Note:10.1007/978-1-4684-6316-3
Contents:Some remarks on the analysis of survival data -- Multivariate failure time data: Representation and analysis -- Analysis of multivariate survival times with non-proportional hazards models -- Analysis of mean and rate functions for
recurrent events -- Extending the Cox model -- Model-based and/or marginal analysis for multiple event-time data -- Artificial insemination by donor: discrete time survival data with crossed and nested random effects -- Interval
censored survival data: A review of recent progress -- Singly and doubly censored current status data with extensions to multistate counting processes -- Additive hazards regression models for survival data -- Some exploratory tools
for survival analysis -- Survival analysis in the regulatory setting -- Proposed strategies for designing and analyzing sepsis trials -- Coarsening at random: characterizations, conjectures, counter-examples -- Sequential models for
coarsening and missingness -- Addresses for Contact Authors -- List of Referees
ISBN:9781468463163
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Lecture Notes in Statistics: 123
Keywords: Statistics , Epidemiology , Biometrics (Biology) , Statistics , Statistics for Life Sciences, Medicine, Health Sciences , Biometrics , Epidemiology
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Call number:SPRINGER-1990-9781461233848:ONLINE Show nearby items on shelf
Title:A Statistical Model Frederick Mosteller’s Contributions to Statistics, Science, and Public Policy
Author(s):
Date:1990
Size:1 online resource (283 p.)
Note:10.1007/978-1-4612-3384-8
Contents:1 Biography -- 2 Bibliography -- Books -- Papers -- Miscellaneous -- Reviews -- 3 Contributions as a Scientific Generalist -- 3.1 Background -- 3.2 Creation and Generalization within Statistics -- 3.3 Overlaps between Statistics and
Other Domains -- 3.4 Establishing Links among Nonstatistical Fields -- 3.5 Links between Research and Applications -- 3.6 Formation of New Fields of Inquiry -- 3.7 Encouragement of Other Scholars -- 3.8 Institutional Leadership in
Science -- Appendix 1: The Mosteller Years at AAAS -- Appendix 2: Resolution Adopted by the Board of Trustees of the Russell Sage Foundation -- 4 Contributions to Mathematical Statistics -- 4.1 Systematic Statistics -- 4.2 Slippage
Tests: “The Problem of the Greatest One” -- 4.3 Stochastic Models for Learning -- 4.4 Products of Random Matrices, Computer Image Generation, and Computer Learning -- 4.5 Number Theory—Statistics for the Love of It -- References -- 5
Contributions to Methodology and Applications -- 5.1 Introduction -- 5.2 Public Opinion Polls and Other Survey Data -- 5.3 Quality Control -- 5.4 On Pooling Data -- 5.5 Thurstone-Mosteller Model for Paired Comparisons -- 5.6 Measuring
Utility -- 5.7 Measuring Pain -- 5.8 The Analysis of Categorical Data -- 5.9 Statistics and Sports -- 5.10 The Jackknife -- 5.11 Meta-Analysis in the Social and Medical Sciences -- 5.12 Summary -- Appendix: Frederick Mosteller, Social
Science, and the Meta-Analytic Age -- References -- 6 Fred as Educator -- 6.1 Precollege Education—Advocacy and Action -- 6.2 Face-to-Face Teaching in Traditional and Nontraditional Settings -- 6.3 On Teaching Teachers -- 6.4
Evaluating Education and Evaluating Educational Evaluations -- 6.5 The Learning Process—Research, Teaching, and Practice -- 6.6 Continuing Education -- References -- 7 Fred at Harvard -- 7.1 Introduction -- 7.2 In the Department of
Social Relations -- 7.3 In the Department of Statistics -- 7.4 In the Kennedy School of Government and the Law School -- 7.5 In the School of Public Health -- 7.6 The “Statistician’s Guide to Exploratory Data Analysis” -- 7.7 Epilogue
-- Appendix: Excerpts from “Some Topics of Interest to Graduate Students in Statistics” -- References -- 8 Reviews of Book Contributions -- Sampling Inspection, W. Allen Wallis -- The Pre-Election Polls of 1948, Richard F. Link --
Statistical Problems of the Kinsey Report, James A. Davis -- Stochastic Models for Learning, Paul W. Holland -- Probability with Statistical Applications, Emanuel Parzen -- The Federalist, John W. Pratt -- Fifty Challenging Problems in
Probability, Joseph I. Naus -- On Equality of Educational Opportunity, Richard J. Light -- Federal Statistics, Yvonne M. Bishop -- National Assessment of Educational Progress, Lyle V. Jones -- Statistics: A Guide to the Unknown,
Gudmund R. Iversen -- Sturdy Statistics, Ralph B. D’Agostino -- Statistics by Example, Janet D. Elashoff -- Weather and Climate Modification, Michael Sutherland -- Costs, Risks, and Benefits of Surgery, Howard H. Hiatt -- Statistics
and Public Policy, Michael A. Stoto -- Data Analysis and Regression, Sanford Weisberg -- Data for Decisions, William B. Fairley -- Statistician’s Guide to Exploratory Data Analysis, Persi Diaconis -- Beginning Statistics with Data
Analysis, John D. Emerson -- Biostatistics in Clinical Medicine, Joel C. Kleinman
ISBN:9781461233848
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Mathematics , Applied mathematics , Engineering mathematics , Mathematics , Applications of Mathematics
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Call number:SPRINGER-1988-9783642469008:ONLINE Show nearby items on shelf
Title:Compstat Proceedings in Computational Statistics 8th Symposium held in Copenhagen 1988
Author(s):
Date:1988
Size:1 online resource (451 p.)
Note:10.1007/978-3-642-46900-8
Contents:Keynote Papers -- Parallel Linear Algebra in Statistical Computations -- Three Examples of Computer-Intensive Statistical Inference -- Non-Parametric Estimation -- Efficient Nonparametric Smoothing in High Dimensions Using Interactive
Graphical Techniques -- A Boundary Modification of Kernel Function Smoothing, with Application to Insulin Absorption Kinetics -- A Roughness Penalty Regression Approach for Statistical Graphics -- Projection Pursuit -- Detecting
Structures by Means of Projection Pursuit -- Confidence Regions for Projection Pursuit Density Estimates -- A Robustness Property of the Projection Pursuit Methods in Sampling from Separably Dependent Random Vectors -- Graphical
Techniques -- Graphical Modelling with Large Numbers of Variables: An Application of Principal Components -- Some Graphical Displays for Square Tables -- Data Plotting Methods for Checking Multivariate Normality and Related Ideas --
Computer-aided Illustration of Regression Diagnostics -- Computer Guided Diagnostics -- Expert Systems -- How should the Statistical Expert System and its User see Each Other? -- Towards a Probabilistic Analysis of MYCIN-like Expert
Systems -- An Expert System Accepting Knowledge in a form of Statistical Data -- Building a Statistical Expert System with Knowledge Bases of Different Levels of Abstraction -- An Expert System for the Interpretation of Results of
Canonical Covariance Analysis -- Building a Statistical Knowledge Base: A Discussion of the Approach used in the Development of THESEUS, a Statistical Expert System -- Prince: An Expert System for Nonlinear Principal Components
Analysis -- Expert Systems for Non-Linear Modelling: Progress and Prospects -- Inside a Statistical Expert System: Statistical Methods Employed in the ESTES System -- An Implementation of an EDA Expert System in Prolog Environment --
Automatic Acquisition of Knowledge Base from Data without Expert: ESOD (Expert Sytem from Observational Data) -- Experiments with Probabilistic Consultation Systems -- Statistical Consultants and Statistical Expert Systems -- On
Inference Process -- Identification Keys, Diagnostic Tables and Expert Systems -- Languages and Packages -- Adding new Statistical Techniques to Standard Software Systems: A Review -- Funigirls: A Prototype Functional Programming
Language for the Analysis of Generalized Linear Models -- Blinwdr: An APL-function Library for Interactively Solving the Problem of Robust and Bounded Influence Regression -- Computational Intensive Methods -- Exact Non-Parametric
Significance Tests -- Resampling Tests of Statistical Hypotheses -- Clustering Based on Neural Network Processing -- Decision Tree Classifier for Speech Recognition -- Algorithms -- Efficient Sampling Algorithms and Balanced Samples --
Recursive Partition in Biostatistics: Stability of Trees and Choice of the Most Stable Classification -- Generating Rules by Means of Regression Analysis -- A New Algorithm for Matched Case-Control Studies with Applications to Additive
Models -- An Algorithm for the Approximation of N-Dimensional Distributions -- Further Recursive Algorithms for Multidimensional Table Computation -- Statistical Methods -- Nonlinear Regression: Methodological and Software Aspects --
Comparing Sensitivity of Models to Missing Data in the GMANOVA -- A Modelling Approach to Multiple Correspondence Analysis -- Multidimensional Scaling on the Sphere -- A Monte Carlo Evaluation of the Methods for Estimating the
Parameters of the Generalized Lambda Distribution -- Statistical Guidance for Model Modification in Covariance Structure Analysis -- Similarities Functions -- Robust Bayesian Regression Analysis with HPD-Regions -- Time Series --
Estimation of ARMA Process Parameters and Noise Variance by Means of a Non-Linear Filtering Algorithm -- Autoregressive Models with Latent Variables -- An Algorithm for Time Series Decomposition Using StateSpace Models with Singular
Transition Matrix -- Statistical Data Bases and Survey Processing -- New Perspectives in Computer Assisted Survey Processing -- Multiple Imputation for Data-Base Construction -- Grasp: A Complete Graphical Conceptual Language for
Definition and Manipulation of Statistical Databases -- Experimental Design -- New Algorithmic and Software Tools for D-Optimal Design Computation in Nonlinear Regression -- Econometric Computing -- Model-Building on Micro Computers:
Spreadsheets or Specific Software -- Late Arrivals -- Three Examples of Computer-Intensive Statistical Inference -- New Computer Procedures for Generating Optimal Mixture Designs on Finite Design Spaces -- Screening Based Exclusively
on Experts Opinions -- Address list of Authors
ISBN:9783642469008
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Mathematics , Probabilities , Mathematics , Probability Theory and Stochastic Processes
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