Variable selection in regression models using principal components

Abstract

In this paper, we present a new stepwise method for selecting predictor variables in linear regression models. This method is an extension of principal component regression, and it consists of iteratively selecting original predictor variables one at a time from repeatedly selected subsets of principal components. Application of the method is discussed and illustrated by an example.

Keywords:

• multicollinearity
• variable selection
• principal components