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Main Authors: Grünwald, Peter, Hao, Yunda, Balsubramani, Akshay
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2412.17554
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author Grünwald, Peter
Hao, Yunda
Balsubramani, Akshay
author_facet Grünwald, Peter
Hao, Yunda
Balsubramani, Akshay
contents 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 Csiszár-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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id arxiv_https___arxiv_org_abs_2412_17554
institution arXiv
publishDate 2024
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spellingShingle Growth-Optimal E-Variables and an extension to the multivariate Csiszár-Sanov-Chernoff Theorem
Grünwald, Peter
Hao, Yunda
Balsubramani, Akshay
Information Theory
Statistics Theory
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 Csiszár-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).
title Growth-Optimal E-Variables and an extension to the multivariate Csiszár-Sanov-Chernoff Theorem
topic Information Theory
Statistics Theory
url https://arxiv.org/abs/2412.17554