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  • 标题:Recursive density estimators based on Robbins–Monro’s scheme and using Bernstein polynomials
  • 本地全文:下载
  • 作者:Slaoui, Yousri ; Slaoui, Yousri ; Jmaei, Asma
  • 期刊名称:Statistics and Its Interface
  • 印刷版ISSN:1938-7989
  • 电子版ISSN:1938-7997
  • 出版年度:2019
  • 卷号:12
  • 期号:3
  • 页码:439-455
  • DOI:10.4310/19-SII561
  • 出版社:International Press
  • 摘要:In this paper, we consider the alleviation of the boundary problem when the probability density function has bounded support. We apply Robbins–Monro’s algorithm and Bernstein polynomials to construct a recursive density estimator. We study the asymptotic properties of the proposed recursive estimator. We then compare our proposed recursive estimator with many others estimators. Finally, we confirm our theoretical result through a simulation study and then using two real datasets..
  • 关键词:density estimation; stochastic approximation algorithm; Bernstein polynomial; smoothing; curve fitting
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