用贝叶斯效用重新思考成功概率

IF 1.8 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Fulvio De Santis, Stefania Gubbiotti, Francesco Mariani
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引用次数: 0

摘要

在混合频率-贝叶斯方法中,试验的成功概率(PoS)是测试的传统幂函数相对于预先分配给审查参数的设计的期望值。然而,这个定义并不是明确的,一些建议也不乏潜在的缺陷。这些问题与这样一个事实有关,即这些定义都是基于拒绝零假设的概率,而不是基于选择正确假设的概率,无论是零假设还是可选假设。在本文中,我们提出了一种统一的决策理论方法,该方法将PoS定义为试验的期望效用(u-PoS),即在两个假设之间做出正确选择的期望概率。这个提议比以前的PoS定义在概念上有优势;此外,当设计先验为零假设分配正概率时,它产生较小的最优样本量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Rethinking Probability of Success as Bayes Utility

Rethinking Probability of Success as Bayes Utility

In the hybrid frequentist-Bayesian approach, the probability of success (PoS) of a trial is the expected value of the traditional power function of a test with respect to a design prior assigned to the parameter under scrutiny. However, this definition is not univocal and some of the proposals do not lack of potential drawbacks. These problems are related to the fact that such definitions are all based on the probability of rejecting the null hypothesis rather than on the probability of choosing the correct hypothesis, be it the null or the alternative. In this article, we propose a unifying, decision-theoretic approach that yields a new definition of PoS as the expected utility of the trial (u-PoS), that is, as the expected probability of making the correct choice between the two hypotheses. This proposal shows a conceptual advantage over previous definitions of PoS; moreover, it produces smaller optimal sample sizes whenever the design prior assigns positive probability to the null hypothesis.

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来源期刊
Biometrical Journal
Biometrical Journal 生物-数学与计算生物学
CiteScore
3.20
自引率
5.90%
发文量
119
审稿时长
6-12 weeks
期刊介绍: Biometrical Journal publishes papers on statistical methods and their applications in life sciences including medicine, environmental sciences and agriculture. Methodological developments should be motivated by an interesting and relevant problem from these areas. Ideally the manuscript should include a description of the problem and a section detailing the application of the new methodology to the problem. Case studies, review articles and letters to the editors are also welcome. Papers containing only extensive mathematical theory are not suitable for publication in Biometrical Journal.
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