k 个正常群体的客观贝叶斯多重检验

Pub Date : 2024-07-29 DOI:10.1007/s42952-024-00281-4
Sang Gil Kang, Yongku Kim
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引用次数: 0

摘要

本文提出了正态模型的客观贝叶斯多重检验程序。考虑所有真假零假设的配置是一项具有挑战性的任务,本文通过根据贝叶斯因子对零假设进行排序来解决这一问题。对于 k 个空假设,这种方法将用于后验搜索的比较模型的大小从 \(2^k\) 减少到 \(k+1/)。此外,还建立了所提出的多重检验程序的一致性,并用模拟和实际例子分析了它们的行为。此外,在所有可能的真假有序零假设配置中,将所提出的程序与经典和贝叶斯多重检验程序进行了比较。
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Objective Bayesian multiple testing for k normal populations

This article proposes objective Bayesian multiple testing procedures for a normal model. The challenging task of considering all the configurations of true and false null hypotheses is addressed here by ordering the null hypotheses based on their Bayes factors. This approach reduces the size of the compared models for posterior search from \(2^k\) to \(k+1\), for k null hypotheses. Furthermore, the consistency of the proposed multiple testing procedures is established and their behavior is analyzed with simulated and real examples. In addition, the proposed procedures are compared with classical and Bayesian multiple testing procedures in all the possible configurations of true and false ordered null hypotheses.

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