Randomised -values and nonparametric procedures in multiple testing

Joshua Habiger and Edsel Peña

Journal of Nonparametric Statistics, 2011, vol. 23, issue 3, 583-604

Abstract: The validity of many multiple hypothesis testing procedures for false discovery rate (FDR) control relies on the assumption that P-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, admits P-value statistics to satisfy the uniformity condition even when test statistics have discrete distributions. This allows nonparametric tests to be used to generate P-value statistics satisfying the uniformity condition. The resulting multiple testing procedures are therefore endowed with robustness properties. Simulation studies suggest that nonparametric randomised test P-values allow for these FDR methods to perform better when the model for the observables is nonparametric or misspecified.

Date: 2011
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Citations: View citations in EconPapers (4)

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DOI: 10.1080/10485252.2010.482154

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