Non-asymptotic sub-Gaussian error bounds for hypothesis testing

Yanpeng Li and Boping Tian

Statistics & Probability Letters, 2022, vol. 189, issue C

Abstract: Using the sub-Gaussian norm of the Bernoulli random variable, this paper presents the explicit and informative error lower bounds for binary and multiple hypothesis testing in terms of the KL divergence non-asymptotically. Some numerical comparisons are also demonstrated.

Keywords: Pinsker’s bound; KL divergence; Sub-Gaussian; Fano’s inequality (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.spl.2022.109586

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