Nonparametric estimator of false discovery rate based on Bernšteǐn polynomials

Zhong Guan, Baolin Wu, Hongyu Zhao

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Under a local dependence assumption about the p-values, an estimator of the proportion TTQ of true null hypotheses, having a closed-form expression, is derived based on Bernstein polynomial density estimation. A nonparametric estimator of false discovery rate (FDR) is then obtained. These estimators are proved to be consistent, asymptotically unbiased, and normal. Confidence intervals for π0 and the FDR are also given. The usefulness of the proposed method is demonstrated through simulations and its application to a microarray dataset.

Original languageEnglish (US)
Pages (from-to)905-923
Number of pages19
JournalStatistica Sinica
Volume18
Issue number3
StatePublished - Jul 1 2008

Keywords

  • Bernšteǐn polynomials
  • Bioinformatics
  • Density estimation
  • False discovery rate
  • Local dependence
  • Microarray
  • Mixture model
  • Multiple comparison

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