A new multiple testing method in the dependent case
A new multiple testing method in the dependent case
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DOI：
10.1214/08-aos616
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发表时间：
2009-06
影响因子：
4.5
通讯作者：
A. Cohen;H. Sackrowitz;Minya Xu
中科院分区：
文献类型：
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作者：
A. Cohen;H. Sackrowitz;Minya Xu
最流行的多重检验程序是基于单个检验统计量的P值的逐步程序。其中包括Benjamini-Hochberg的错误发现率（FDR）控制程序[J. Roy。中央集权主义者Soc. Ser. B 57（1995）289 - 300]及其后代。即使对于需要依赖数据的模型，也使用基于边缘分布的P值。与此类方法不同，新方法在所有阶段都考虑了依赖性。此外，P值程序往往缺乏一个直观的凸性，这是必要的可接受性。此外，新方法在计算上是可行的。如果测试的数量很大，而真正的替代品的比例小于25%，那么模拟表明新方法的明显偏好。详细介绍了模型的应用程序，例如测试对照组（或任何类内相关模型）的治疗，测试变化点和测试相关性连续时的平均值。
The most popular multiple testing procedures are stepwise procedures based on P-values for individual test statistics. Included among these are the false discovery rate (FDR) controlling procedures of Benjamini―Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289―300] and their offsprings. Even for models that entail dependent data, P-values based on marginal distributions are used. Unlike such methods, the new method takes dependency into account at all stages. Furthermore, the P-value procedures often lack an intuitive convexity property, which is needed for admissibility. Still further, the new methodology is computationally feasible. If the number of tests is large and the proportion of true alternatives is less than say 25 percent, simulations demonstrate a clear preference for the new methodology. Applications are detailed for models such as testing treatments against control (or any intra-class correlation model), testing for change points and testing means when correlation is successive.