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A New Confidence Interval for the Mean of a Bounded Random Variable

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arxiv 1905.06208 v2 pith:LJLISAQO submitted 2019-05-15 math.ST cs.LGmath.PRstat.TH

A New Confidence Interval for the Mean of a Bounded Random Variable

classification math.ST cs.LGmath.PRstat.TH
keywords confidenceintervalboundedmeanrandomvariablemethodsamples
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present a new method for constructing a confidence interval for the mean of a bounded random variable from samples of the random variable. We conjecture that the confidence interval has guaranteed coverage, i.e., that it contains the mean with high probability for all distributions on a bounded interval, for all samples sizes, and for all confidence levels. This new method provides confidence intervals that are competitive with those produced using Student's t-statistic, but does not rely on normality assumptions. In particular, its only requirement is that the distribution be bounded on a known finite interval.

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Cited by 2 Pith papers

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  1. An Exact Distribution-Free Test for Means of Nonnegative Random Variables

    math.ST 2026-07 accept novelty 7.5

    K(X) formed from uniform Dirichlet weights is a valid finite-sample distribution-free p-value for H0: EXi≤1 for all independent nonnegative Xi, proving Gaffke's conjecture.

  2. On the Order-Conditional Optimality of Gaffke's Bound

    math.ST 2026-07 conditional novelty 6.0

    Gaffke's bound is Buehler-optimal within the class of lower confidence bounds that induce its own sample ordering, for the maximum marginal mean of independent nonnegative variables.