Generalized Linear Models
A Bayesian Perspective
Chapman & Hall/CRC Biostatistics Series

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

Part 1 Extending the GLMs. Part 2 Categorical and longitudinal data. Part 3 Semiparametric approaches. Part 4 Model diagnositics and value selection in GLMs. Part 5 Challenging problems in GLMs

Statisticians, biostatisticians, and applied statisticians in the pharmaceutical industry and medical centers, and graduate-level students in these disciplines.

Dipak K. Dey, Sujit K. Ghosh , Bani K. Mallick