多重比较的几种校正方法求答案

发布网友发布时间：2022-04-24 17:40

共1个回答

热心网友时间：2023-10-27 08:35

举个例子：如要在同一数据集上检验两个独立的假设，显著水平设为常见的0.05。此时用于检验该两个假设应使用更严格的 0.025。即0.05* (1/2)。该方法是由Carlo Emilio Bonferroni发展的，因此称Bonferroni校正。　　这样做的理由是基于这样一个事实：在同一数据集上进行多个假设的检验，每20个假设中就有一个可能纯粹由于概率，而达到0.05的显著水平。　　
维基百科原文：　　Bonferroni correction: Bonferroni correction states that if an experimenter is testing n independent hypotheses on a set of data, then the statistical significance level that should be used for each hypothesis separately is 1/n times what it would be if only one hypothesis were tested.
　　For example, to test two independent hypotheses on the same data at 0.05 significance level, instead of using a p value threshold of 0.05, one would use a stricter threshold of 0.025.
　　The Bonferroni correction is a safeguard against multiple tests of statistical significance on the same data, where 1 out of every 20 hypothesis-tests will appear to be significant at the α = 0.05 level purely e to chance. It was developed by Carlo Emilio Bonferroni.
　　A less restrictive criterion is the rough false discovery rate giving (3/4)0.05 = 0.0375 for n = 2 and (21/40)0.05 = 0.02625 for n = 20.
Benjamini and Hochberg在1995年第一次提出了FDR(False Discovery Rate)的概念，其出发点就是基于Bonferroni的保守性，并给出了控制FDR的方法（这算是FDR控制方法的祖师爷了）。不过他们的方法也有其保守性。所以随后人们开始研究更加powerful的方法，现有的方法有Storey的, Broberg的，Dalmasso的，Guan的，Strimmer的等等等等。Benjamini的方法是将FDR控制在一个level以下，而之后所有的方法都在试图精确地估计FDR。所以后来的这些方法都要powerful一些。不过他们所付出的代价就是robustness。据说Storey方法是最流行的FDR control procere(For details see Storey's paper published ON PNAS ,2003)。