On the expected runtime of multiple testing algorithms with bounded error

Georg Hahn

Statistics & Probability Letters, 2020, vol. 165, issue C

Abstract: Consider testing multiple hypotheses in the setting where the p-values of all hypotheses are unknown and thus have to be approximated using Monte Carlo simulations. One class of algorithms published in the literature for this scenario provides guarantees on the correctness of their testing result through the computation of confidence statements on all approximated p-values. This article focuses on the expected runtime of such algorithms and derives a variety of finite and infinite expected runtime results.

Keywords: Algorithm; Bounded error; Computational effort; Finite expected runtime; Multiple testing (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.spl.2020.108844

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