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arXiv:2210.01948math.ST

Game-theoretic statistics and safe anytime-valid inference

Aaditya Ramdas, Peter Grünwald, Vladimir Vovk, Glenn Shafer

Abstract

Safe anytime-valid inference (SAVI) provides measures of statistical evidence and certainty -- e-processes for testing and confidence sequences for estimation -- that remain valid at all stopping times, accommodating continuous monitoring and analysis of accumulating data and optional stopping or continuation for any reason. These measures crucially rely on test martingales, which are nonnegative martingales starting at one. Since a test martingale is the wealth process of a player in a betting game, SAVI centrally employs game-theoretic intuition, language and mathematics. We summarize the SAVI goals and philosophy, and report recent advances in testing composite hypotheses and estimating functionals in nonparametric settings.

arXiv comments
25 pages. Under review. ArXiv does not compile/space some references properly
Journal reference
DOI
Subjects
math.STStatistics Theory cs.GTComputer Science and Game Theory cs.ITInformation Theory stat.MEMethodology (Statistics)

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    • If thresholding of P-values is the issue, E-values -- a recent, much more elegant, easier to work with, and more robust alternative to P-values -- solve this. https://arxiv.org/abs/2312.08040 https://arxiv.org/abs/2205.00901 https://arxiv.org/abs/2210.01948…

      dccsillagView on HNParent context

  1. Annoying A/B testing mistakes 1 comment
    • The biggest mistake engineers make is determining sample sizes. It is not trivial to determine the sample size for a trial without prior knowledge of effect sizes. Instead of waiting for a fixed sample size, I would recommend using a sequential testing framework: set a stopping…

      kimukasetsuView on HNParent context

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