title: RandomisedP-values and nonparametric procedures in multiple testing.
abstract: The validity of many multiple hypothesis testing procedures for false discovery rate (FDR) control relies on the assumption thatP-value statistics are uniformly distributed under the null hypotheses. However, this assumption fails if the test statistics have discrete distributions or if the distributional model for the observables is misspecified. A stochastic process framework is introduced that, with the aid of a uniform variate, admitsP-value statistics to satisfy the uniformity condition even when test statistics have discrete distributions. This allows nonparametric tests to be used to generateP-value statistics satisfying the uniformity condition. The resulting multiple testing procedures are therefore endowed with robustness properties. Simulation studies suggest that nonparametric randomised testP-values allow for these FDR methods to perform better when the model for the observables is nonparametric or misspecified.
authors.affiliation: Department of Statistics, 216 LeConte College, University of South Carolina, Columbia, SC 29208, USA.
authors.forename: Joshua D
authors.initials: JD
authors.lastname: Habiger
authors.affiliation: Department of Statistics, 216 LeConte College, University of South Carolina, Columbia, SC 29208, USA.
authors.forename: Edsel A
authors.initials: EA
authors.lastname: Peña
keywords: P-value statistics; false discovery rate; microarray; multiple testing; randomisation