Correlation and large-scale simultaneous significance testing
Correlation and large-scale simultaneous significance testing
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DOI：
10.1198/016214506000001211
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发表时间：
2007-03-01
影响因子：
3.7
通讯作者：
Efron, Bradley
中科院分区：
文献类型：
--
作者：
Efron, Bradley
大规模的假设检验问题，需要同时考虑数百或数千个检验统计量 z(i)，在当前的实践中已经变得很常见。流行分析方法的应用，例如错误发现率技术，不需要 z(i) 的独立性，但在高相关情况下其准确性可能会受到影响。本文介绍了用于评估大规模测试中相关性的大小和效果的计算和理论方法。一个简单的理论可以识别 z(i) 阶统计量的单一综合相关性度量。该理论涉及同时显着性检验的零分布的正确选择及其对推理的影响。
Large-scale hypothesis testing problems, with hundreds or thousands of test statistics z(i) to consider at once, have become familiar in current practice. Applications of popular analysis methods, such as false discovery rate techniques, do not require independence of the z(i)'s, but their accuracy can be compromised in high-correlation situations. This article presents computational and theoretical methods for assessing the size and effect of correlation in large-scale testing. A simple theory leads to the identification of a single omnibus measure of correlation for the z(i)'s order statistic. The theory relates to the correct choice of a null distribution for simultaneous significance testing and its effect on inference.