Game-Theoretic Statistics and Safe Anytime-Valid Inference

Aaditya Ramdas, Peter Grünwald, Vladimir Vovk, Glenn Shafer

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)576-597
Number of pages22
JournalStatistical Science
Volume38
Issue number4
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Mathematics
  • Statistics, Probability and Uncertainty

Keywords

  • Test martingales
  • Ville’s inequality
  • confidence sequence
  • e-process
  • nonparametric composite hypothesis testing
  • optional stopping
  • reverse information projection
  • universal inference

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