Abstract:
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.