4th Edition

Generalized Linear Models and Extensions Fourth Edition

By James W. Hardin, Joseph M. Hilbe Copyright 2018
598 Pages
by Stata Press

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions—a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these models in Stata by using specialized commands (for example, logit for logit models), fitting them as... Read more

Foundations of Generalized Linear Models. 

GLMs. 

GLM estimation algorithms.  

Analysis of fit.

Continuous Response Models. 

The Gaussian family. 

The gamma family. 

The inverse Gaussian family.  

The power family and link. 

Binomial Response Models. 

The binomial–logit family. 

The general binomial family. 

The problem of overdispersion. 

Count Response Models. 

The Poisson family. 

The negative binomial family.  

Other count-data models. 

Multinomial Response Models. 

Unordered-response family.  

The ordered-response family.  

Extensions to the GLM.  

Extending the likelihood. 

Clustered data. 

Bivariate and multivariate models. 

Bayesian GLMs. 

Stata Software.  

Programs for Stata.  

Data synthesis.

Biography

James W. Hardin is a professor and the Biostatistics division head in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also the associate dean for Faculty Affairs and Curriculum of the Arnold School of Public Health at the University of South Carolina.

Joseph M. Hilbe was a professor emeritus at the University of Hawaii and an adjunct professor of sociology and statistics at Arizona State University.