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

Abstract

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.

Journal

Journal of the Japanese Society of Computational Statistics   [List of Volumes]

Journal of the Japanese Society of Computational Statistics 8(1), 57-68, 1995-12  [Table of Contents]

Japanese Society of Computational Statistics

References:  18

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Codes

  • NII Article ID (NAID) :
    110001235622
  • NII NACSIS-CAT ID (NCID) :
    AA10823693
  • Text Lang :
    ENG
  • Article Type :
    ART
  • ISSN :
    09152350
  • Databases :
    CJP  NII-ELS 

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