Structure–Adaptive Sequential Testing for Online False Discovery Rate Control
提出结构自适应序贯检验规则，用于在线控制错误发现率，通过自适应学习最优阈值和优化alpha-wealth分配，在保证渐近有效性的同时显著提升统计功效。
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 Conventional simultaneous testing rules are no longer applicable due to the more stringent error constraints and absence of future data.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 discovery rate (FDR) control. A key element in our proposal is a new alpha-investing 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 SAST is asymptotically valid for online FDR control and achieves substantial power gain over existing online testing rules.