The control of the false discovery rate in fixed sequence multiple testing

Gavin Lynch, Wenge Guo, Sanat K. Sarkar, Helmut Finner

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Controlling false discovery rate (FDR) is a powerful approach to multiple testing. In many applications, the tested hypotheses have an inherent hierarchical structure. In this paper, we focus on the fixed sequence structure where the testing order of the hypotheses has been strictly specified in advance. We are motivated to study such a structure, since it is the most basic of hierarchical structure, yet it is often seen in real applications such as statistical process control and streaming data analysis. We first consider a conventional fixed sequence method that stops testing once an acceptance occurs, and develop such a method controlling FDR under both arbitrary and negative dependencies. The method under arbitrary dependency is shown to be unimprovable without losing control of FDR and, unlike existing FDR methods; it cannot be improved even by restricting to the usual positive regression dependence on subset (PRDS) condition. To account for any potential mistakes in the ordering of the tests, we extend the conventional fixed sequence method to one that allows more but a given number of acceptances. Simulation studies show that the proposed procedures can be powerful alternatives to existing FDR controlling procedures. The proposed procedures are illustrated through a real data set from a microarray experiment.

Original languageEnglish (US)
Pages (from-to)4649-4673
Number of pages25
JournalElectronic Journal of Statistics
Volume11
Issue number2
DOIs
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Arbitrary dependence
  • False discovery rate
  • Fixed sequence
  • Multiple testing
  • Negative association
  • P-values
  • PRDS property

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