Theoretical considerations of model selection method based on a minimization of an information criterion
Project/Area Number |
25540012
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Research Category |
Grant-in-Aid for Challenging Exploratory Research
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Allocation Type | Multi-year Fund |
Research Field |
Statistical science
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Research Institution | Hiroshima University |
Principal Investigator |
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Co-Investigator(Renkei-kenkyūsha) |
FUJISAWA HIRONORI 統計数理研究所, 数理・推論研究系, 教授 (00301177)
NINOMIYA YOSHIYUKI 九州大学, マス フォア インダストリ研究所, 准教授 (50343330)
|
Project Period (FY) |
2013-04-01 – 2016-03-31
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Project Status |
Completed (Fiscal Year 2015)
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Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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Keywords | モデル選択 / 情報量規準 / 多変量線形回帰モデル / 高次元データ / 一致性 / 大標本高次元漸近理論 / 有効性 |
Outline of Final Research Achievements |
In this research, we study model selection method based on a minimization of an information criterion. There are many information criteria. We evaluate theoretical properties of an information criterion used for model selection by a large sample asymptotic theory such that the sample size goes to infinity and a high-dimensional and large sample asymptotic theory such that the sample size and the dimension of a vector of response variables go to infinity simultaneously. From obtained results, we provide standards of judgment with regard to deciding an information criterion.
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Report
(4 results)
Research Products
(20 results)