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文章基本信息

  • 标题:Optimal bandwidth selection for semi-recursive kernel regression estimators
  • 作者:Yousri Slaoui
  • 期刊名称:Statistics and Its Interface
  • 印刷版ISSN:1938-7989
  • 电子版ISSN:1938-7997
  • 出版年度:2016
  • 卷号:9
  • 期号:3
  • 页码:375-388
  • DOI:10.4310/SII.2016.v9.n3.a11
  • 出版社:International Press
  • 摘要:In this paper we propose an automatic selection of the bandwidth of the semi-recursive kernel estimators of a regression function defined by the stochastic approximation algorithm. We showed that, using the selected bandwidth and some special stepsizes, the proposed semi-recursive estimators will be very competitive to the nonrecursive one in terms of estimation error but much better in terms of computational costs. We corroborated these theoretical results through simulation study and a real dataset.
  • 关键词:nonparametric regression; stochastic approximation algorithm; smoothing; curve fitting
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