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Generalized Linear Models : A Bayesian Perspective

By: (Edited by) Bani K. Mallick , (Edited by) Dipak K. Dey , (Edited by) Sujit K. Ghosh

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Ksh 23,400.00

Format: Hardback or Cased Book

ISBN-10: 0824790340

ISBN-13: 9780824790349

Series: Chapman & Hall/CRC Biostatistics Series

Publisher: Taylor & Francis Inc

Imprint: CRC Press Inc

Country of Manufacture: US

Country of Publication: GB

Publication Date: May 25th, 2000

Print length: 442 Pages

Weight: 970 grams

Product Classification: Probability & statistics
Maths for computer scientists

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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, this work considers parametric and semiparametric approaches to overdispersed GLMs.
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.

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