Bayesian methods for repeated measures
著者
書誌事項
Bayesian methods for repeated measures
（Chapman & Hall/CRC biostatistics series）
CRC Press/Taylor & Francis Group, c2016
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注記
"A Chapman & Hall book"
Includes bibliographical references and index
内容説明・目次
内容説明
Analyze Repeated Measures Studies Using Bayesian Techniques
Going beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many inferential methods for analyzing repeated measures in various scientific areas, especially biostatistics.
The author takes a practical approach to the analysis of repeated measures. He bases all the computing and analysis on the WinBUGS package, which provides readers with a platform that efficiently uses prior information. The book includes the WinBUGS code needed to implement posterior analysis and offers the code for download online.
Accessible to both graduate students in statistics and consulting statisticians, the book introduces Bayesian regression techniques, preliminary concepts and techniques fundamental to the analysis of repeated measures, and the most important topic for repeated measures studies: linear models. It presents an in-depth explanation of estimating the mean profile for repeated measures studies, discusses choosing and estimating the covariance structure of the response, and expands the representation of a repeated measure to general mixed linear models. The author also explains the Bayesian analysis of categorical response data in a repeated measures study, Bayesian analysis for repeated measures when the mean profile is nonlinear, and a Bayesian approach to missing values in the response variable.
目次
Introduction to the Analysis of Repeated Measures. Review of Bayesian Regression Methods. Foundation and Preliminary Concepts. Linear Models for Repeated Measures and Bayesian Inference. Estimating the Mean Profile of Repeated Measures. Correlation Patterns for Repeated Measures. General Linear Mixed Model. Repeated Measures for Categorical Data. Nonlinear Models and Repeated Measures. Bayesian Techniques for Missing Data.
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