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Generalized Linear Models: A Bayesian Perspective
     

Generalized Linear Models: A Bayesian Perspective

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

ISBN-10: 0824790340

ISBN-13: 9780824790349

Pub. Date: 05/25/2000

Publisher: Taylor & Francis

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

Overview

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.

Product Details

ISBN-13:
9780824790349
Publisher:
Taylor & Francis
Publication date:
05/25/2000
Series:
Chapman & Hall/CRC Biostatistics Series
Pages:
440
Product dimensions:
6.90(w) x 10.10(h) x 1.00(d)

Table of Contents

I General  Overview                                                                                                                              1
Generalized Linear Models: A Bayesian View 3
Random Effects in Generalized Linear Mixed Models 23
Prior Elicitation and Variables Selection for Generalized Linear Mixed Models 41
II Extending the GLMs 55
Dynamic Generalized Linear Models 57
Bayesian Approaches for Overdispersion in Generalized Linear Models 73
Bayesian Generalized Linear Models for Inference About Small Areas 89
III Categorical and Longitudinal Data 111
Bayesian Methods for Correlated Binary Data 113
Bayesian Analysis for Correlated Ordinal Data Models 133
Bayesian Methods for Time Series Count Data Item 159
Response Modeling 173
Developing and Applying Medical Practice Guidelines Following Acute Myocardial Infarction: A Case Study Using Bayesian Probit and Logit Models 195
IV Semiparametric Approaches 215
Semiparametric Generalized Linear Models: Bayesian Approaches 217
Binary Response Regression with Normal Scale Mixture Links 231
Binary Regression Using Data Adaptive Robust Link Functions 243
A Mixture-Model Approaches to the Analysis of Survival Data 255
V Model Diagnostics and Variable Selection in GLMs 271
Bayesian Variable Selection Using the Gibbs Sampler 273
Bayesian Methods for Variables Selection in the Cox Model 287
Bayesian Model Diagnostics for Correlated Binary Data 313
VI Challenging Approaches in GLMs 329
Bayesian Errors-in-Variables Modeling 331
Bayesian Analysis of Compositional Data 349
Classification Trees 365
Modeling and Inference for Point-Referenced Binary Spatial Data 373
Bayesian Graphical Models and Software for GLMs 387
Index 407

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