Anytime‑valid sequential testing for elicitable functionals via supermartingales
"Using regret bounds from Online Convex Optimization, we obtain rigorous guarantees on the asymptotic power of the tests for a wide range of alternative hypotheses. Our results allow for bounded and unbounded data distributions, assuming that a sub‑ψ tail bound is satisfied."