Sparse principal component regression with adaptive loading

Shuichi Kawano, Hironori Fujisawa, Toyoyuki Takada, Toshihiko Shiroishi

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To address this problem, we propose the sparse principal component regression (SPCR) that is a one-stage procedure for PCR. SPCR enables us to adaptively obtain sparse principal component loadings that are related to the response variable and select the number of principal components simultaneously. SPCR can be obtained by the convex optimization problem for each parameter with the coordinate descent algorithm. Monte Carlo simulations and real data analyses are performed to illustrate the effectiveness of SPCR.

Original languageEnglish
Pages (from-to)192-203
Number of pages12
JournalComputational Statistics and Data Analysis
Volume89
DOIs
Publication statusPublished - Sept 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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