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RandomisedP-values and nonparametric procedures in multiple testing
Journal of Nonparametric Statistics ( IF 1.2 ) Pub Date : 2011-09-01 , DOI: 10.1080/10485252.2010.482154
Joshua D Habiger 1 , Edsel A Peña 1
Affiliation  

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.

中文翻译:

多重检验中的随机 P 值和非参数程序

许多用于错误发现率 (FDR) 控制的多假设检验程序的有效性依赖于 P 值统计在零假设下均匀分布的假设。但是,如果测试统计量具有离散分布或可观察量的分布模型指定错误,则此假设将失败。引入了一个随机过程框架,在均匀变量的帮助下,即使测试统计量具有离散分布,也允许 P 值统计量满足均匀性条件。这允许使用非参数检验来生成满足均匀性条件的 P 值统计量。因此,由此产生的多重测试程序具有稳健性。
更新日期:2011-09-01
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