Variable Selection in Multivariate Linear Regression Models with Fewer Observations than the Dimension
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- Yamamura Mariko
- Department of Clinical Medicine (Biostatistics), School of Pharmacy, Kitasato University
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- Yanagihara Hirokazu
- Department of Mathematics, Graduate School of Science, Hiroshima University
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- Srivastava Muni S.
- Department of Statistics, University of Toronto
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This paper deals with the selection of variables in multivariate linear regression models with fewer observations than the dimension by using Akaike's information criterion (AIC). It is well known that the AIC cannot be defined when the dimension of an observation is larger than the sample size, since an ordinary estimator of the covariance matrix becomes singular. By replacing the ordinary estimator of the covariance matrix with its ridge-type estimator, we propose a new AIC for selecting variables of multivariate linear regression models even though the dimension of an observation is larger than the sample size. The bias correction term of AIC is evaluated from a remarkable asymptotic theory based on the dimension and the sample size approaching to ∞ simultaneously. By conducting numerical studies, we verify that our new criteria perform well.
収録刊行物
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- 応用統計学
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応用統計学 39 (1), 1-19, 2010
応用統計学会
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詳細情報 詳細情報について
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- CRID
- 1390001204443184256
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- NII論文ID
- 10026049107
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- NII書誌ID
- AN00330942
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- ISSN
- 18838081
- 02850370
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- NDL書誌ID
- 10674088
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 使用不可