Bayesian Statistics Seminar
North Carolina State University
presents
Mr. Dhruv Sharma
North Carolina State University
"A Decision Theoretic Approach to the Variable Selection Problem"
ABSTRACT
Variable selection within a canonical linear regression framework is a fundamental activity in the analysis of data sets. A common strategy for this problem has been to select a model that minimizes a penalized sum of squares criterion by a constraint optimization method. However the optimality of such a procedure has not been formally studied within a formal decision theoretic framework. This article presents a formal solution by obtaining the Bayes estimator corresponding to a loss function suitable for the problem. The solution is shown to be valid for the "large p small n case" case but would require a version of the stochastic search algorithm to compute the optimal estimator. We present simulation studies to compare the performance of our estimator with some of the popular variable selection approaches available. The proposed method is also illustrated using a real data set.
Wednesday, April, 18, 2007
4:00 - 5:00 pm
208 Patterson Hall