Generalized linear models - a Bayesian perspective

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Generalized linear models - a Bayesian...

Generalized linear models - a Bayesian perspective

Name: Generalized linear models - a Bayesian perspective
ISBN: 9780824790349

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

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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, covering random effects in generalized linear mixed models (GLMMs) with explained examples. Considers parametric and semiparametric approaches to overdispersed GLMs, applies Bayesian GLMs to US mortality data, and presents methods of analyzing correlated binary data using latent variables. Describes and analyzes item response modeling for categorical data, and provides variable selection methods using the Gibbs sampler for Cox models. Dey is professor and head of the department of statistics at the University of Connecticut-Storrs

Catégories:

Mathematics

Année:

2000

Edition:

Editeur::

CRC Press

Langue:

english

Pages:

442

ISBN 10: