Abstract
PCA (principal component analysis) is a technique for visualizing a continuous data with p(≥3) variables through dimension reduction. As result, the n observations are summarized by k(=2,3,…) principal component scores. The purpose of this article is to develop the method for finding a subset of q(≤p) variables best predicting k principal component scores obtained from all p variables. We propose an algorithm for variable selection method using regression imputation technique for principal component analysis.
