Error control in tree structured hypothesis testing

IF 4.4 2区 数学 Q1 STATISTICS & PROBABILITY Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-11-25 DOI:10.1002/wics.1603
J. Miecznikowski, Jiefei Wang
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Abstract

This manuscript reviews some recent and popular error control methods for tree structured hypothesis testing. We review a common setting/definition for hypotheses arranged in a tree structure and we discuss two common Type I errors present in multiple testing: family wise error rates (FWERs) and false discovery rate (FDR). We also contrast these methods with a recent development designed to control the false selection rate (FSR). We discuss the algorithms used to implement these error controls and the strategies used to navigate tree structures in light of these errors. We highlight the assumptions necessary in these strategies, summarize the available R software packages to implement these approaches, and show them at work on an example.
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树结构假设检验中的误差控制
本文综述了一些最近流行的树结构假设检验误差控制方法。我们回顾了树形结构中假设的常见设置/定义,并讨论了多重测试中出现的两种常见I型错误:家庭明智错误率(fwer)和错误发现率(FDR)。我们还将这些方法与最近设计用于控制错误选择率(FSR)的方法进行了对比。我们讨论了用于实现这些错误控制的算法以及用于根据这些错误导航树结构的策略。我们强调了这些策略中必要的假设,总结了实现这些方法的可用R软件包,并在一个示例中展示了它们的工作原理。
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
31
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