Bayesian multiple testing under sparsity for polynomial-tailed distributions

Xueying Tang, Ke Li, Malay Ghosh

Research output: Contribution to journalArticlepeer-review

Abstract

This paper considers Bayesian multiple testing under sparsity for polynomial-tailed distributions satisfying a monotone likelihood ratio property. Included in this class of distributions are the Student's t, the Pareto, and many other distributions. We prove some general asymptotic optimality results under fixed and random thresholding. As examples of these general results, we establish the Bayesian asymptotic optimality of several multiple testing procedures in the literature for appropriately chosen false discovery rate levels. We also show by simulation that the Benjamini-Hochberg procedure with a false discovery rate level different from the asymptotically optimal one can lead to high Bayes risk.

Original languageEnglish (US)
Pages (from-to)1225-1242
Number of pages18
JournalStatistica Sinica
Volume27
Issue number3
DOIs
StatePublished - Jul 2017
Externally publishedYes

Keywords

  • Asymptotic optimality
  • Benjamini-Hochberg procedure
  • False discovery rate
  • Pareto distribution
  • Student's t distribution

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Bayesian multiple testing under sparsity for polynomial-tailed distributions'. Together they form a unique fingerprint.

Cite this