An Introduction to Generalized Linear Mixed Models

Prof. Charles McCulloch
University of California, San Francisco
October 27-28


The class of generalized linear mixed models (GLMMs) is a broad class of statistical models generalizing both linear mixed models and generalized linear models (GLMs). As such it is capable of accommodating nonlinear responses, correlated data and non-normal distributions. This makes it quite useful in practice. For example, GLMMs give a natural way to specify a correlated data model for binary data. After briefly introducing generalized linear models Professor McCulloch will describe the extension to GLMMs. The focus in the course will be on approaches to modeling, methods of estimation and inference, and available software. The concepts will be illustrated on a number of examples.

The short course is a free service offered to conference registrants. No additional fees are required beyond conference registration.