Generalized Linear Models and Extensions, Fourth Edition - James Hardin

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. 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  bayesmhCommand of Stata 14, and the updated  bayes prefix syntax available in Stata 15.


About the Author
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.