| Project/Area Number |
14580352
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Okayama University of Science |
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Principal Investigator |
MORI Yuichi Okayama University of Science, Faculty of Informatics, Professor, 総合情報学部, 教授 (80230085)
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| Co-Investigator(Kenkyū-buntansha) |
IIZUKA Masaya Okayama University, Faculty of Environmental Science and Technology, Lecturer, 環境理工学部, 講師 (60322236)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,000,000 (Direct Cost: ¥3,000,000)
Fiscal Year 2004: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2003: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2002: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Variable selection software / Principal component analysis / Factor analysis / Correspondence analysis / Web application / Macros for statistical packages / 項目選択 / 情報量規準 / 適合度規準 / 変数選択ソフトウェアVASMM / 変数選択マクロ / Computer Intensive Methods |
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| Research Abstract |
We study variable selection in multivariate methods without external variables such as principal component analysis (PCA), factor analysis (FA) and correspondence analysis (CA). In this study we discuss the followings.
1.Existing methods of variable selection in multivariate methods without external variables are collected from literatures. They are reviewed and then published as overviews of variable selection in our papers and on the web (see 4)
2.New selection criteria in PCA, FA and CA are proposed. In PCA modified principal component as a selection criterion is evaluated, information on how many variables should be used is discussed using computer-intensive methods, and a selection criterion in the sense of regression is proposed and evaluated by AIC. In FA, criteria using RV-coefficient to evaluate the closeness between a configuration of factor scores based on all variables and one on selected variables is considered. In CA, a selection criterion using RV-coefficient for the closeness on two case scores and criteria using ordinary goodness of fit in CA are discussed.
3.Applying the proposed criteria to real data sets, they are found that every criterion can be used in the real situations and that selection procedures such as forward-backward stepwise selection provide similar results to all possible selection.
4.Variable selection environment"VASMM"(VAriable Selection in Multivariate Methods) is established on the web (http://mo161.soci.ous.ae.jp/vasmm/) to provide information on variable selection in multivariate methods without external variables and on-line selection function. Macros for general statistical packages such as R and XploRe are developed. Using these tools variable selection can be performed anytime and anywhere.
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