树(集合论)
多重比较问题
数学
航程(航空)
依赖关系(UML)
选择(遗传算法)
I类和II类错误
字错误率
帧(网络)
错误发现率
关系(数据库)
算法
统计假设检验
统计
数据挖掘
计算机科学
机器学习
人工智能
基因
生物
数学分析
复合材料
材料科学
电信
生物化学
作者
Marina Bogomolov,Christine B. Peterson,Yoav Benjamini,Chiara Sabatti
出处
期刊:Biometrika
[Oxford University Press]
日期:2020-09-28
卷期号:108 (3): 575-590
被引量:27
标识
DOI:10.1093/biomet/asaa086
摘要
Summary We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the $p$-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer.
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