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Growth-Optimal E-Variables and an extension to the multivariate Csisz\'ar-Sanov-Chernoff Theorem

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arxiv 2412.17554 v2 pith:DBH4PPW7 submitted 2024-12-23 cs.IT math.ITmath.STstat.TH

classification cs.ITmath.ITmath.STstat.TH
keywords meanspacee-variablesnullar-sanov-chernoffcasecsiszdimensionalgrowth-optimal
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abstract

We consider growth-optimal e-variables with maximal e-power, both in an absolute and relative sense, for simple null hypotheses for a $d$-dimensional random vector, and multivariate composite alternatives represented as a set of $d$-dimensional means $\meanspace_1$. These include, among others, the set of all distributions with mean in $\meanspace_1$, and the exponential family generated by the null restricted to means in $\meanspace_1$. We show how these optimal e-variables are related to Csisz\'ar-Sanov-Chernoff bounds, first for the case that $\meanspace_1$ is convex (these results are not new; we merely reformulate them) and then for the case that $\meanspace_1$ `surrounds' the null hypothesis (these results are new).

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Cited by 2 Pith papers

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

  1. Strong duality for the GROW criterion

    math.ST 2026-06 unverdicted novelty 7.0 of 10

    The GROW value for bounded e-variables equals the minimal relative entropy between weak-* closed convex hulls of arbitrary composite null and alternative sets.

  2. Testing maximum entropy models with e-values

    stat.ME 2025-09 conditional novelty 7.0 of 10

    It derives an exact growth-rate optimal e-variable for microcanonical maximum entropy tests and shows this e-variable is a valid, near-optimal approximation for canonical tests.

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