[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CSAM.2015.22.2.173

Principal Component Regression by Principal Component Selection

Lee, Hosung (Department of Statistics, Hankuk University of Foreign Studies)
Park, Yun Mi (Department of Statistics, Hankuk University of Foreign Studies)
Lee, Seokho (Department of Statistics, Hankuk University of Foreign Studies)

Publication Information

Communications for Statistical Applications and Methods / v.22, no.2, 2015 , pp. 173-180 More about this Journal

Abstract

We propose a selection procedure of principal components in principal component regression. Our method selects principal components using variable selection procedures instead of a small subset of major principal components in principal component regression. Our procedure consists of two steps to improve estimation and prediction. First, we reduce the number of principal components using the conventional principal component regression to yield the set of candidate principal components and then select principal components among the candidate set using sparse regression techniques. The performance of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression and partial least square regression) using synthetic and real datasets.

Keywords

Biased estimation; dimension reduction; penalized regression; principal component regression; principal component selection;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
Cited By KSCI

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