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文章基本信息

  • 标题:Distribution-free multiple testing
  • 本地全文:下载
  • 作者:Ery Arias-Castro ; Shiyun Chen
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2017
  • 卷号:11
  • 期号:1
  • 页码:1983-2001
  • DOI:10.1214/17-EJS1277
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:We study a stylized multiple testing problem where the test statistics are independent and assumed to have the same distribution under their respective null hypotheses. We first show that, in the normal means model where the test statistics are normal Z-scores, the well-known method of Benjamini and Hochberg [4] is optimal in some asymptotic sense. We then show that this is also the case of a recent distribution-free method proposed by Barber and Candès [14]. The method is distribution-free in the sense that it is agnostic to the null distribution — it only requires that the null distribution be symmetric. We extend these optimality results to other location models with a base distribution having fast-decaying tails.
  • 关键词:Multiple testing;distribution-free procedure, Benjamini-Hochberg procedure;asymptotic optimality;false discovery rate (FDR) control.
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