Variable Selection in Logistic Discrimination Based on Local Likelihood

Abstract

We consider the variable selection problem in the nonlinear discriminant procedure using local likelihood. The local likelihood method is an effective technique for analyzing data with complex structure, and various bandwidth selection methods have been suggested in recent years. Variable selection in a nonlinear model, however, is more complex than bandwidth selection, since the optimal bandwidth depends on the combination of the variables. We propose a technique for variable selection using generalized information criteria in logistic discrimination based on local likelihood. We derive the logistic discrimination method with a sample covariance matrix to account for the correlation of the variables. Real data examples are given to examine the effectiveness of our technique.