JSM 2005 Online Program

Abstract #304238

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	448
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	General Methodology
Abstract - #304238
Title:	Variable Selection in Finite Mixture of Regression Models
Author(s):	Abbas Khalili*+ and Jiahua Chen
Companies:	University of Waterloo and University of Waterloo
Address:	Department of Statistics, Waterloo, ON, N2L 3G1, Canada
Keywords:	variable selection ; Finite mixture distirbutions ; Finite mixture of regression models
Abstract:	The problem of variable selection is an important model selection problem in statistical applications. We consider the problem of variable selection in Finite Mixture of Regression (FMR) models. These models have two interesting features. Like any regression model, the FMR models are used to study the relationship between a set of potential covariates and a response variable. Further, in an FMR model, the conditional distribution of the response variable given the covariates is a finite mixture distribution. The FMR models have been used in fields such as machine learning, marketing, and social sciences. Often, at the beginning of a study, a large number of potential covariates are of interest. This produces a large and complex FMR model that is not desirable in statistical modeling. Thus, some variable selection decisions need to be made. We extend the idea of LASSO and SCAD-type estimators proposed by Tibshirani (1996) and Fan and Li (2001) in the context of nonmixture regression models to perform variable selection in FMR models. Performance of the method is studied theoretically and via simulations.

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