Bayesian (mean) most powerful tests

被引:3
|
作者
Zhang, Jin [1 ]
机构
[1] Yunnan Univ, Sch Math & Stat, Kunming 650091, Yunnan, Peoples R China
关键词
Bayes factor; hypothesis testing; likelihood-ratio test; Neyman-Pearson lemma; prior distribution; MODEL SELECTION;
D O I
10.1111/anzs.12171
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
A fundamental theorem in hypothesis testing is the Neyman-Pearson (N-P) lemma, which creates the most powerful test of simple hypotheses. In this article, we establish Bayesian framework of hypothesis testing, and extend the Neyman-Pearson lemma to create the Bayesian most powerful test of general hypotheses, thus providing optimality theory to determine thresholds of Bayes factors. Unlike conventional Bayes tests, the proposed Bayesian test is able to control the type I error. To extend Neyman-Pearson lemma, the Bayesian most powerful test is created for general hypotheses.
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页码:43 / 56
页数:14
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