期刊
JOURNAL OF NONPARAMETRIC STATISTICS
卷 23, 期 3, 页码 583-604出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10485252.2010.482154
关键词
multiple testing; randomisation; false discovery rate; microarray; P-value statistics
资金
- NSF [DMS0805809]
- National Institutes of Health (NIH) [RR17698]
- Environmental Protection Agency (EPA) [RD-83241902-0, Y481344]
- Division Of Mathematical Sciences [0805809] Funding Source: National Science Foundation
- NATIONAL CENTER FOR RESEARCH RESOURCES [P20RR017698] Funding Source: NIH RePORTER
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据