Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence
Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence
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DOI：
10.1214/20-ba1196
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发表时间：
2021-03-01
影响因子：
4.4
通讯作者：
Berger, James O.
中科院分区：
文献类型：
--
作者：
Chang, Sean;Berger, James O.
考虑了相依检验统计量检验互斥假设的问题。贝叶斯和频率论方法的多重性控制进行了研究和比较，以帮助获得理解的影响，检验统计量依赖于每种方法。贝叶斯方法被证明具有优良的频率论属性，并认为是最有效的方式获得频率论多重性控制，而不牺牲权力，当有相当大的检验统计量的依赖。
The problem of testing mutually exclusive hypotheses with dependent test statistics is considered. Bayesian and frequentist approaches to multiplicity control are studied and compared to help gain understanding as to the effect of test statistic dependence on each approach. The Bayesian approach is shown to have excellent frequentist properties and is argued to be the most effective way of obtaining frequentist multiplicity control, without sacrificing power, when there is considerable test statistic dependence.