| Project/Area Number |
15500182
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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 | Tokyo Institute of Technology |
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Principal Investigator |
MIYAKAWA Masami Tokyo Institute of Technology, Graduate School of Decision Science and Technology, Industrial Engineering and Management, Professor, 大学院・社会理工学研究科, 教授 (90157595)
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| Co-Investigator(Kenkyū-buntansha) |
YAJIMA Yasutoshi Tokyo Institute of Technology, Graduate School of Decision Science and Technology, Industrial Engineering and Management, Assistant Professor, 大学院・社会理工学研究科, 助教授 (80231645)
黒木 学 大阪大学, 大学院・基礎工学研究科, 助教授 (60334512)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 2004: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2003: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Discrimination / Mahalanobis distance / Partial least squares / Multicollinearity / Causal analysis / Total effect / 条件付き介入 |
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| Research Abstract |
There are several fundamentally different situations in which it may be desired to select a subset from a large number of variables. In this research, firstly, the problem of selection of variables is investigated in asymmetric discrimination using Mahalanobis distance. As a countermeasure against multicollinearity, two kinds of distance are proposed which enable us to distinguish the redundant measurements from the inherent linear constraints of a normal group. In the asymmetric discrimination problem, not only the unconditional error rate but also the conditional error rates of a future observation from a normal group are also investigated. Secondly, several basic properties of the predicted values obtained by the partial least squares method are discussed, Furthermore, in a causal diagram, graphical criteria for selecting both covariates and variables caused by a response variable are proposed in order to identify total effects in studies with the unobserved response variable.
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