Generalized Linear Models: A Bayesian Perspective

Author:   Dipak K. Dey ,  Sujit K. Ghosh ,  Bani K. Mallick
Publisher:   Taylor & Francis Ltd
ISBN:  

9780367398606


Pages:   442
Publication Date:   01 November 2019
Format:   Paperback
Availability:   In Print   Availability explained
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Generalized Linear Models: A Bayesian Perspective


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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.

Full Product Details

Author:   Dipak K. Dey ,  Sujit K. Ghosh ,  Bani K. Mallick
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.820kg
ISBN:  

9780367398606


ISBN 10:   0367398605
Pages:   442
Publication Date:   01 November 2019
Audience:   College/higher education ,  General/trade ,  Tertiary & Higher Education ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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

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