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Variable Selection in Principal Component Analysis

In: Statistical Methods for Biostatistics and Related Fields

Author

Listed:
  • Yuichi Mori
  • Masaya Iizuka
  • Tomoyuki Tarumi
  • Yutaka Tanaka

Abstract

While there exist several criteria by which to select a reasonable subset of variables in the context of PCA, we introduce herein variable selection using criteria in Tanaka and Mori (1997)’s modified PCA (M.PCA) among others. In order to perform such variable selection via XploRe, the quantlib vaspca, which reads all the necessary quantlets for selection, is first called, and then the quantlet mpca is run using a number of selection parameters. In the first four sections we present brief explanations of variable selection in PCA, an outline of M.PCA and flows of four selection procedures, based mainly on Tanaka and Mori (1997)’s, Mori (1997), Mori, Tarumi and Tanaka (1998) and Iizuka et al. (2002a). In the last two sections, we illustrate the quantlet mpca and its performance by two numerical examples.

Suggested Citation

  • Yuichi Mori & Masaya Iizuka & Tomoyuki Tarumi & Yutaka Tanaka, 2007. "Variable Selection in Principal Component Analysis," Springer Books, in: Statistical Methods for Biostatistics and Related Fields, chapter 14, pages 265-283, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-32691-5_14
    DOI: 10.1007/978-3-540-32691-5_14
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