ABSTRACT

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

part II|2 pages

Extending the GLMs

part V|2 pages

Model Diagnostics and Variable Selection in GLMs

part VI|2 pages

Challenging Approaches in GLMs

chapter 19|18 pages

Bayesian Errors-in-Variables Modeling

chapter 20|16 pages

Bayesian Analysis of Compositional Data

chapter 21|8 pages

Classification Trees