Control of the false discovery rate under dependence using the bootstrap and subsampling
Control of the false discovery rate under dependence using the bootstrap and subsampling
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DOI：
10.1007/s11749-008-0126-6
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发表时间：
2008-11-01
期刊：
影响因子：
1.3
通讯作者：
Wolf, Michael
中科院分区：
文献类型：
--
作者：
Romano, Joseph P.;Shaikh, Azeem M.;Wolf, Michael
本文考虑了在控制错误发现率的同时检验原假设的问题。Benjamini和Hochberg（J. R. Stat. Soc. Ser. B 57（1）：289-300，1995）提供了一种用于在p值独立的假设下基于每个零假设的p值来控制FDR的方法。随后的研究表明，在p值联合分布的较弱假设下，该过程是有效的。还开发了在p值的联合分布没有假设的情况下有效的相关程序。然而，这些程序都没有包含有关检验统计量的依赖结构的信息。本文开发的方法控制的FDR弱假设下，将这些信息，并通过这样做，能够更好地检测虚假的零假设。我们通过模拟研究和两个实证应用说明了这一特性。特别是，自举方法是有竞争力的方法，需要独立性，如果独立性，但它优于这些方法的依赖。
This paper considers the problem of testing s null hypotheses simultaneously while controlling the false discovery rate (FDR). Benjamini and Hochberg (J. R. Stat. Soc. Ser. B 57(1):289-300, 1995) provide a method for controlling the FDR based on p-values for each of the null hypotheses under the assumption that the p-values are independent. Subsequent research has since shown that this procedure is valid under weaker assumptions on the joint distribution of the p-values. Related procedures that are valid under no assumptions on the joint distribution of the p-values have also been developed. None of these procedures, however, incorporate information about the dependence structure of the test statistics. This paper develops methods for control of the FDR under weak assumptions that incorporate such information and, by doing so, are better able to detect false null hypotheses. We illustrate this property via a simulation study and two empirical applications. In particular, the bootstrap method is competitive with methods that require independence if independence holds, but it outperforms these methods under dependence.