期刊
ANNALS OF STATISTICS
卷 41, 期 4, 页码 1716-1741出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/13-AOS1123
关键词
Bayes factor; Jeffreys-Lindley paradox; objective Bayes; one-parameter exponential family model; Neyman-Pearson lemma; nonlocal prior density; uniformly most powerful test; Higgs boson
资金
- National Cancer Institute [R01 CA158113]
Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size. In this article, the notion of uniformly most powerful tests is extended to the Bayesian setting by defining uniformly most powerful Bayesian tests to be tests that maximize the probability that the Bayes factor, in favor of the alternative hypothesis, exceeds a specified threshold. Like their classical counterpart, uniformly most powerful Bayesian tests are most easily defined in one-parameter exponential family models, although extensions outside of this class are possible. The connection between uniformly most powerful tests and uniformly most powerful Bayesian tests can be used to provide an approximate calibration between p-values and Bayes factors. Finally, issues regarding the strong dependence of resulting Bayes factors and p-values on sample size are discussed.
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