BIASED CROSS-VALIDATION IN A KERNEL REGRESSION ESTIMATION
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概要
- 論文の詳細を見る
This article is concerned with the problem of choosing a bandwidth for nonparametric regression. We consider a method based on an biased estimate of mean average squared error. It is seen that the bandwidth chosen by biased cross-validation method, is asymptotically optimal and has small sample variability. In a simulation study, we show that this bandwidth is closer to optimum bandwidth than other bandwidths when the underlying regression function is sufficiently smooth.
- 日本計算機統計学会の論文
著者
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Oh Jong
Department of Obstetrics and Gynecology, Microbiology and Immunology, Chonnam National University Me
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Kim Byung
Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan, University School of Medici
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Oh Jong
Department Of Obstetrics And Gynecology Microbiology And Immunology Chonnam National University Medi
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Lee Jee
Samsung Data System
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Park B
Department Of Computer Science And Statistics College Of Natural Sciences Seoul National University
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Lee Jee
Samsung Cheil Hospital Breast Center
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Park B.
Department Of Computer Science And Statistics College Of Natural Sciences Seoul National University
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Kim Byung
Department Of Applied Mathematics College Of Applied Science Kyung Hee University
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Oh Jong
Department Of Computer Science And Statistics College Of Natural Sciences Kunsan National University
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Kim Byung
Department Of Aerospace Engineering School Of Engineering Nagoya University
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Oh Jong
Department of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi 440-746, Republic of Korea
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