The Benjamini-Hochberg method with infinitely many contrasts in linear models

Peter H. Westfall

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Benjamini and Hochberg's method for controlling the false discovery rate is applied to the problem of testing infinitely many contrasts in linear models. Exact, easily calculated critical values are derived, defining a new multiple comparisons method for testing contrasts in linear models. The method is adaptive, depending on the data through the F-statistic, like the Waller-Duncan Bayesian multiple comparisons method. Comparisons with Scheffé's method are given, and the method is extended to the simultaneous confidence intervals of Benjamini and Yekutieli.

Original languageEnglish
Pages (from-to)709-719
Number of pages11
Issue number3
StatePublished - Sep 2008


  • False coverage rate
  • False discovery rate
  • Multiple comparisons
  • Multiple testing
  • Scheffé's method
  • Waller-Duncan method


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