Abstract:In many scientific applications, hypotheses are generated and tested continuously in a stream. We develop a framework for improving online multiple testing procedures with false discovery rate (FDR) control under arbitrary dependence. Our approach is two-fold: we construct methods via the online e-closure principle, as well as a novel formulation of online compound e-values that is defined through donations. This yields strict power improvements over state-of-the-art e-value and p-value procedures while retaining FDR control. We further derive algorithms that compute the decision at time $t$ in $O(\log t)$ time, and we demonstrate improved empirical performance on synthetic and real data.
From: Ziyu Xu [view email]
[v1]
Wed, 25 Mar 2026 20:06:38 UTC (191 KB)
[v2]
Mon, 6 Jul 2026 01:00:19 UTC (127 KB)
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