Generalized Linear Models ─ A Bayesian Perspective

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. Annotation c. Book News, Inc., Portland, OR (booknews.com)