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arxiv: 1801.09418 · v3 · pith:XXNAQJZDnew · submitted 2018-01-29 · 📊 stat.ME

Test Martingales for bounded random variables

classification 📊 stat.ME
keywords randomtestmartingalesassuranceboundedconfidenceconstructfinancial
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Given a random sample from a random variable $T$ which is bounded from above, $T\le\tau$ a.s., we define processes that are positive supermartingales if $E(T)\ge\mu$. Such processes are called test martingales. Tests of the supermartingale hypothesis implicitly test the hypothesis $H_0:E(T)\ge\mu$. We construct test martingales that lead to tests with power 1. We also construct confidence upper bounds. We extend the techniques to testing $H_0:E(T)=\mu$ and constructing confidence intervals. In financial auditing random sampling is proposed as one of the possible techniques to gather enough assurance to be able to state that there is no 'material' misstatement in a financial report. The goal of our work is to provide a mathematical context that could represent such process of gathering assurance by means of repeated random sampling.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means

    stat.ML 2026-05 unverdicted novelty 7.0

    A Bayesian predictive model adaptively constructs asymptotically log-optimal confidence sequences for bounded means using test martingales.

  2. Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means

    stat.ML 2026-05 unverdicted novelty 7.0

    A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.