Generalized Linear Models

A Bayesian Perspective

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

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English

CRC Press Inc
25 May 2000

Probability & statistics; Maths for computer scientists

Series: 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.

Edited by: Dipak K. Dey, Sujit K. Ghosh, Bani K. Mallick, Bani K. Mallick
Imprint: CRC Press Inc
Country of Publication: United States
Volume: v. 5
Dimensions: Height: 246mm, Width: 174mm, Spine: 26mm
Weight: 970g
ISBN: 9780824790349
ISBN 10: 0824790340
Series: Chapman & Hall/CRC Biostatistics Series
Pages: 442
Publication Date: 25 May 2000
Audience: College/higher education , Professional and scholarly , Professional & Vocational , A / AS level , Further / Higher Education
Format: Hardback
Publisher's Status: Active

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

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

Reviews for Generalized Linear Models: A Bayesian Perspective

both accessible and valuable. Anyone interested in applying the Bayesian GLMs should have a copy of this book on their shelf. -Statistical Methods in Medical Research