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混合无记忆源的一阶和二阶假设检验

First- and Second-Order Hypothesis Testing for Mixed Memoryless Sources.

作者信息

Han Te Sun, Nomura Ryo

机构信息

National Institute of Information and Communications Technology (NICT), Tokyo 184-8795, Japan.

School of Network and Information, Senshu University, Kanagawa 214-8580, Japan.

出版信息

Entropy (Basel). 2018 Mar 6;20(3):174. doi: 10.3390/e20030174.

Abstract

The first- and second-order optimum achievable exponents in the simple hypothesis testing problem are investigated. The optimum achievable exponent for type II error probability, under the constraint that the type I error probability is allowed asymptotically up to ε , is called the ε -optimum exponent. In this paper, we first give the second-order ε -optimum exponent in the case where the null hypothesis and alternative hypothesis are a mixed memoryless source and a stationary memoryless source, respectively. We next generalize this setting to the case where the alternative hypothesis is also a mixed memoryless source. Secondly, we address the first-order ε -optimum exponent in this setting. In addition, an extension of our results to the more general setting such as hypothesis testing with mixed general source and a relationship with the general compound hypothesis testing problem are also discussed.

摘要

研究了简单假设检验问题中的一阶和二阶最优可达指数。在允许第一类错误概率渐近高达ε的约束下,第二类错误概率的最优可达指数称为ε最优指数。在本文中,我们首先给出了原假设和备择假设分别为混合无记忆源和平稳无记忆源情况下的二阶ε最优指数。接下来,我们将此设置推广到备择假设也是混合无记忆源的情况。其次,我们讨论了此设置下的一阶ε最优指数。此外,还讨论了将我们的结果扩展到更一般的设置,如混合一般源的假设检验以及与一般复合假设检验问题的关系。

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