Correlation and large-scale simultaneous significance testing

被引:280
作者
Efron, Bradley [1 ]
机构
[1] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
correlated processes; empirical null; false discovery rate; microarray;
D O I
10.1198/016214506000001211
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:93 / 103
页数:11
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