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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