Statistics and Data Science Seminar
Dr. Ed Vonesh
Northwestern University
Generalized Linear and Nonlinear Models for Correlated Data: Overcoming Apparent Limitations in SAS
Abstract: Correlated response data, either discrete (nominal, ordinal, counts), continuous or a combination thereof, occur in numerous disciplines and more often than not, require the use of statistical models that are nonlinear in the parameters of interest. Such models include generalized linear and generalized nonlinear models both of which can be further classified according to whether they are marginal or mixed-effects models. In this talk we briefly describe the different types of correlated response data and models encountered in practice. As some of the models can be quite complicated, there will often appear to be certain modeling limitations with available software. For users of SAS, such limitations would appear to include 1) how to conduct likelihood-based inference for nonlinear mixed-effects models with intra-subject correlation; 2) how to fit nonlinear mixed-effects models to data assuming non-Gaussian random effects; and 3) how to fit marginal generalized linear models to correlated response data using second order generalized estimating equations or maximum likelihood estimation. The focus of this talk will be on illustrating how one can fit mixed-effects models in SAS when the random effects are non-Gaussian. Following the work of Nelson et. al. (2006), the approach entails applying probability integral transformations when evaluating an integrated log-likelihood function. This technique is illustrated through an application that requires one to jointly model two dependent variables using a shared non-Gaussian random effect. It requires the user to be familiar with nonlinear mixed-effects models in general and also with how one can fit such models using the SAS procedure NLMIXED.
Wednesday October 31, 2012 at 4:00 PM in SEO 636