Generalized Linear Models and Extensions

by James W. Hardin, Joseph M. Hilbe

The fourth edition of Generalized Linear Models and Extensions gives a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. The text stands out in its coverage of the derivation of the GLM families and their foremost links, but it also guides the reader in applying the various models to real data. This edition has new sections on bivariate and multivariate models including bivariate count data models estimated via copula functions and models based on bivariate distributions put forward by Famoye and by Marshall and Olkin. In addition, there are new sections on Bayesian GLMs illustrating background, the estimation of models using the bayesmh command of Stata 14, and the updated bayes prefix syntax available in Stata 15.

1164 pages, Kindle Edition

Published April 6, 2018

Reviews

[Stephen Cranney · Rating 2 out of 5]

Why are stats specialists usually incapable at explaining their knowledge to human beings? If you're going to explain something rather statistically simple, it doesn't help to start it off with a solid page of Greek characters. The fact that I knew how to explain the stuff I did know in simpler (and more concise) terms made me much less willing to try to slog through the stuff I didn't know.