Non-asymptotic sub-Gaussian error bounds for hypothesis testing

被引：0

作者：

Li, Yanpeng ^{[1
]}

Tian, Boping ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Math, Harbin 150001, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2022年 / 189卷

关键词：

Pinskers bound; KL divergence; Sub-Gaussian; Fanos inequality;

D O I：

10.1016/j.spl.2022.109586

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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. (C) 2022 Elsevier B.V. All rights reserved.

引用

页数：5