This paper presents closed BH, a uniform improvement of the False Discovery Rate controlling method of Benjamini and Hochberg (BH). Closed BH is valid under the same assumption of Positive Regression Dependency on a Subset (PRDS) as BH, but also under an alternative and weaker minimal sufficient condition. As a uniform improvement, closed BH never rejects fewer hypotheses than BH, but it may reject quite a few more. An increase in power is observed especially when the number of false null hypotheses is large. The novel method is constructed using the e-Closure principle, a recently derived general principle for multiple testing. The method is implemented in the eClosure package in R.

翻译：本文提出了封闭BH方法，这是对本雅明尼和霍赫伯格提出的错误发现率控制方法（BH）的统一改进。封闭BH方法在正回归依赖性子集假设下与BH方法同样有效，同时也在更弱的极小充分条件下成立。作为统一改进，封闭BH方法拒绝的假设数量不会少于BH方法，但可能拒绝更多假设。特别是在错误零假设数量较多时，统计功效的提升尤为显著。该方法基于近期提出的通用多重检验原理——e-闭包原理构建，并在R语言的eClosure包中实现。