Variable Selection in Principal Component Analysis

Yuichi Mori, Masaya Iizuka, Tomoyuki Tarumi and Yutaka Tanaka

Chapter 14 in Statistical Methods for Biostatistics and Related Fields, 2007, pp 265-283 from Springer

Abstract: 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.

Keywords: Principal Component Analysis; Variable Selection; Good Subset; Large Versus; Variable Selection Method (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-32691-5_14

Ordering information: This item can be ordered from
http://www.springer.com/9783540326915

DOI: 10.1007/978-3-540-32691-5_14

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().