AN EFFICIENT ALGORITHM FOR THE NONPARAMETRIC REGRESSION ESTIMATION

抄録

Most bandwidth selectors in nonparametric kernel regression estimation are based on minimization of some function of h, which is related to the resubstitution estimate of the prediction error. But this method consumes large amounts of computer time. This article concerns an efficient computational algorithm for this method when the kernel is symmetric and polynomial functions.

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 5(1), 65-73, 1992-12  [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) :
    110001235591
  • NII書誌ID(NCID) :
    AA10823693
  • 本文言語コード :
    ENG
  • ISSN :
    09152350
  • 収録DB :
    NII-ELS