摘要翻译：
Bonferroni多重测试程序通常被认为在大规模同时测试情况下过于保守，如在微阵列数据分析中出现的情况。本研究的目的是表明，这一普遍的信念是由于过分严格的要求，通常强加于程序，而不是其保守的性质。为了克服其臭名昭著的保守性，我们主张使用Bonferroni选择规则作为控制每个家庭错误率(PFER)的程序。本文首次研究了Bonferroni和Benjamini-Hochberg过程的稳定性。Bonferroni过程在真发现数和总发现数的方差方面都表现出很好的稳定性，这一性质在存在单个$P$-值之间的相关性时尤其重要。Bonferroni程序的稳定性和对PFER的强控制能力使其成为微阵列研究中一个有吸引力的选择。
---
英文标题：
《Control of the mean number of false discoveries, Bonferroni and
  stability of multiple testing》
---
作者：
Alexander Gordon, Galina Glazko, Xing Qiu, Andrei Yakovlev
---
最新提交年份：
2007
---
分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--

---
英文摘要：
  The Bonferroni multiple testing procedure is commonly perceived as being overly conservative in large-scale simultaneous testing situations such as those that arise in microarray data analysis. The objective of the present study is to show that this popular belief is due to overly stringent requirements that are typically imposed on the procedure rather than to its conservative nature. To get over its notorious conservatism, we advocate using the Bonferroni selection rule as a procedure that controls the per family error rate (PFER). The present paper reports the first study of stability properties of the Bonferroni and Benjamini--Hochberg procedures. The Bonferroni procedure shows a superior stability in terms of the variance of both the number of true discoveries and the total number of discoveries, a property that is especially important in the presence of correlations between individual $p$-values. Its stability and the ability to provide strong control of the PFER make the Bonferroni procedure an attractive choice in microarray studies.
---
PDF链接：
https://arxiv.org/pdf/709.0366