Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics

Daniel Yekutieli*, Yoav Benjamini

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

520 Scopus citations

Abstract

A new false discovery rate controlling procedure is proposed for multiple hypotheses testing. The procedure makes use of resampling-based p-value adjustment, and is designed to cope with correlated test statistics. Some properties of the proposed procedure are investigated theoretically, and further properties are investigated using a simulation study. According to the results of the simulation study, the new procedure offers false discovery rate control and greater power. The motivation for developing this resampling-based procedure was an actual problem in meteorology, in which almost 2000 hypotheses are tested simultaneously using highly correlated test statistics. When applied to this problem the increase in power was evident. The same procedure can be used in many other large problems of multiple testing, for example multiple endpoints. The procedure is also extended to serve as a general diagnostic tool in model selection.

Original languageEnglish
Pages (from-to)171-196
Number of pages26
JournalJournal of Statistical Planning and Inference
Volume82
Issue number1-2
DOIs
StatePublished - 1 Dec 1999

Keywords

  • Meteorology
  • Model selection
  • Multiple comparisons
  • Multiple endpoints

Fingerprint

Dive into the research topics of 'Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics'. Together they form a unique fingerprint.

Cite this