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关注这篇论文的作者
Hongjian Wang
Aaditya Ramdas

停止时e-BH程序的任意时点有效错误发现率控制

研究问题
连续监控多个数据流并在全局停止时刻采用e-BH拒绝集合时,为何局部e过程的有效性可能不足以保证错误发现率控制?
方法与思路
作者区分局部与全局信息滤过,分析跨数据流的信息泄漏,并提出使局部e过程同时成为全局e过程的因果条件。
材料陈述的贡献
摘要指出任意依赖下逐时使用e-BH不自动保证停止时有效,并给出排除过去未观测混杂的条件以恢复所述错误发现率保证。
阅读关注点
应核对局部和全局停止时刻的区别、失效反例的信息结构,以及提出的因果条件如何被形式化并用于证明停止时控制。
阅读边界
这是arXiv预印本,不能据此声称已同行评审或期刊接收。摘要未给出条件的完整数学形式,不能将保证推广至任意依赖数据流。

原文摘要

The recent e-Benjamini-Hochberg (e-BH) procedure for multiple hypothesis testing is known to control the false discovery rate (FDR) under arbitrary dependence between the input e-values. This paper points out an important subtlety when applying the e-BH procedure with e-processes, which are sequential generalizations of e-values (where the data are observed sequentially). Since adaptively stopped e-processes are e-values, the e-BH procedure can be repeatedly applied at every time step, and one can continuously monitor the e-processes and the rejection sets obtained. One would hope that the "stopped e-BH procedure" (se-BH) has an FDR guarantee for the rejection set obtained at any stopping time. However, while this is true if the data in different streams are independent, it is not true in full generality, because each stopped e-process is an e-value only for stopping times in its own local filtration, but the se-BH procedure employs a stopping time with respect to a global filtration. This can cause information to leak across time, allowing one stream to know its future by knowing past data of another stream. This paper formulates a simple causal condition under which local e-processes are also global e-processes and thus the se-BH procedure does indeed control the FDR. The condition excludes unobserved confounding from the past and is met under most reasonable scenarios including genomics.

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