BIASED CROSS-VALIDATION IN A KERNEL REGRESSION ESTIMATION

    • Oh Jong Chul
    • Department of Computer Science and Statistics, College of Natural Sciences, Kunsan National University
    • Park B. U.
    • Department of Computer Science and Statistics, College of Natural Sciences, Seoul National University

抄録

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.

収録刊行物

Journal of the Japanese Society of Computational Statistics   [巻号一覧]

Journal of the Japanese Society of Computational Statistics 8(1), 57-68, 1995-12  [この号の目次]

日本計算機統計学会

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各種コード

  • NII論文ID(NAID) :
    110001235622
  • NII書誌ID(NCID) :
    AA10823693
  • 本文言語コード :
    ENG
  • 資料種別 :
    ART
  • ISSN :
    09152350
  • 収録DB :
    CJP書誌  NII-ELS