Large-scale simultaneous hypothesis testing: The choice of a null hypothesis

被引:648
作者
Efron, B [1 ]
机构
[1] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
关键词
empirical Bayes; empirical null hypothesis; local false discovery rate; microarray analysis; unobserved covariates;
D O I
10.1198/016214504000000089
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
Current scientific techniques in genomics and image processing routinely produce hypothesis testing problems with hundreds or thousands of cases to consider simultaneously. This poses new difficulties for the statistician, but also opens new opportunities. In particular, it allows empirical estimation of an appropriate null hypothesis. The empirical null may be considerably more dispersed than the usual theoretical null distribution that would be used for any one case considered separately. An empirical Bayes analysis plan for this situation is developed, using a local version of the false discovery rate to examine the inference issues. Two genomics problems are used as examples to show the importance of correctly choosing the null hypothesis.
引用
收藏
页码:96 / 104
页数:9
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