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.
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页数：5
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