Efron Bradley, Tibshirani Robert
Department of Statistics and Division of Biostatistics, Stanford University, Stanford, California 94305, USA.
Genet Epidemiol. 2002 Jun;23(1):70-86. doi: 10.1002/gepi.1124.
In a classic two-sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian modeling, and the frequentist method of "false discovery rates" proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences.
在一个经典的双样本问题中，人们可能会使用威尔科克森统计量来检验治疗组和对照组受试者之间的差异。类似的微阵列实验会产生数千个威尔科克森统计量，阵列上的每个基因都有一个，这给统计学家带来了一个困难的同时推断情况。我们将讨论针对这个问题的两种推断方法：一种经验贝叶斯方法，它几乎不需要先验贝叶斯建模；以及1995年由本雅明尼和霍赫贝格提出的“错误发现率”的频率论方法。事实证明，这两种方法密切相关，可以一起使用以产生合理的同时推断。
