SEMINARS
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Spring 2007
STATISTICS
COLLOQUIUM
Wednesday, April 18, 2007
3:30-4:00—Refreshments
4:00-5:00—Talk
Yost Hall, Room 101
Sunil Rao, PhD
Associate Professor
Division of Biostatistics and Division of Genetic Epidemiology
Department of Epidemiology and Biostatistics School of Medicine
Case Western Reserve University
Fence Methods for Mixed Model Selection
Many model search strategies involve trading off model fit with model complexity in a penalized goodness of fit measure. Asymptotic properties for these types of procedures in settings like linear regression and ARMA time series have been studied, but these do not naturally extend to non-standard situations such as mixed effects models, where simple definition of the sample size is not meaningful. This talk introduces a new class of strategies, known as fence methods for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from amongst those within the fence according to a criterion which can be made flexible. A variety of fence methods can be constructed, based on the same principle but applied to different situations, including clustered and non-clustered data, linear or generalized linear mixed models, and Gaussian or non-Gaussian random effects. I will illustrate some via simulations and real data analyses. In addition, two variations of the basic fence method will be discussed: oneutilizes a stepwise procedure to handle situations of many predictors; the other, an adaptive approach of choosing a tuning constant involved in the fence method. This is joint work with Jiming Jiang, Zhonghua Gu and Than Nguyen of UC-Davis
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