Bayesian Methods for Repeated Measures

Lyle D. Broemeling

August 1, 2017 by Chapman and Hall/CRC
Reference
ISBN 9781138894044 - CAT# K32857
Series: Chapman & Hall/CRC Biostatistics Series

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Features

  • Explores the Bayesian approach to the analysis of repeated measures
  • Includes the necessary introductory material for understanding Bayesian inference and WinBUGS
  • Incorporates many real examples throughout as well as exercises at the end of each chapter
  • Provides the WinBUGS code online so that readers can implement the code as they progress through the book

Summary

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.

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