论文标题
在线虚假发现率控制的结构自适应顺序测试
Structure-Adaptive Sequential Testing for Online False Discovery Rate Control
论文作者
论文摘要
考虑在下一个数据点到达之前必须做出实时决策的一系列假设的在线测试。错误率需要在{ALL}决策点控制。传统\ emph {同时测试规则}由于更严格的错误限制和未来数据的缺乏，因此不再适用。此外，当在线决策过程 - 当总误差预算（或掌握）耗尽时，可能会停止。这项工作开发了一种新的结构 - 在线虚假发现率（FDR）控制的自适应顺序测试（SAST）规则。我们提案中的一个关键要素是一种新的alpha-投资算法，它精确地表征了顺序决策中的收益和损失。 Sast捕获了数据流的时间变化的时间，以持续的方式自适应地学习最佳阈值，并在不同时间段中优化了α-卫生分配。我们提出了理论和数值结果，以表明所提出的方法对在线FDR控制有效，并且在现有的在线测试规则上实现了可观的功率增益。
Consider the online testing of a stream of hypotheses where a real--time decision must be made before the next data point arrives. The error rate is required to be controlled at {all} decision points. Conventional \emph{simultaneous testing rules} are no longer applicable due to the more stringent error constraints and absence of future data. Moreover, the online decision--making process may come to a halt when the total error budget, or alpha--wealth, is exhausted. This work develops a new class of structure--adaptive sequential testing (SAST) rules for online false discover rate (FDR) control. A key element in our proposal is a new alpha--investment algorithm that precisely characterizes the gains and losses in sequential decision making. SAST captures time varying structures of the data stream, learns the optimal threshold adaptively in an ongoing manner and optimizes the alpha-wealth allocation across different time periods. We present theory and numerical results to show that the proposed method is valid for online FDR control and achieves substantial power gain over existing online testing rules.
