Online multiple hypothesis testing.
Online multiple hypothesis testing.
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DOI：
10.1214/23-sts901
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发表时间：
2023-11-01
期刊：
影响因子：
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通讯作者：
Ramdas A
中科院分区：
文献类型：
--
作者：
Robertson DS;Wason JMS;Ramdas A
现代数据分析经常涉及大规模的假设检验，这自然会产生保持控制适当的I类错误率的问题，例如错误发现率（FDR）。在许多生物医学和技术应用中，一个额外的复杂性是假设以在线方式进行测试，随着时间的推移逐个进行。然而，控制FDR的传统程序（如Benjamini-Hochberg程序）假设所有p值均可在单个时间点进行检验。为了应对这些挑战，在过去的15年里，一个新的方法学领域已经发展起来，展示了如何控制在线多假设检验的错误率。在这个框架中，假设以流的形式出现，在每个时间点，分析师都会根据反对当前假设的证据和之前的拒绝决策来决定是否拒绝当前假设。在本文中，我们提出了一个全面的阐述在线错误率控制的文献，审查的关键理论，以及应用实例的重点。我们还提供了模拟结果比较不同的在线测试算法和最新的概述，已提出的许多方法的扩展。
Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all p-values are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples. We also provide simulation results comparing different online testing algorithms and an up-to-date overview of the many methodological extensions that have been proposed.