Improving online FDR procedures via online analogs of e-closure and compound e-values

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.