Generalized Linear Models : A Bayesian Perspective book cover
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1st Edition

Generalized Linear Models
A Bayesian Perspective





ISBN 9780367398606
Published November 1, 2019 by CRC Press
440 Pages

 
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Book Description

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.

Table of Contents

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

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Editor(s)

Biography

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