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March 2021 On the Existence of Uniformly Most Powerful Bayesian Tests With Application to Non-Central Chi-Squared Tests
Amir Nikooienejad, Valen E. Johnson
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Bayesian Anal. 16(1): 93-109 (March 2021). DOI: 10.1214/19-BA1194


Uniformly most powerful Bayesian tests (UMPBT’s) are an objective class of Bayesian hypothesis tests that can be considered the Bayesian counterpart of classical uniformly most powerful tests. Because the rejection regions of UMPBT’s can be matched to the rejection regions of classical uniformly most powerful tests (UMPTs), UMPBT’s provide a mechanism for calibrating Bayesian evidence thresholds, Bayes factors, classical significance levels and p-values. The purpose of this article is to expand the application of UMPBT’s outside the class of exponential family models. Specifically, we introduce sufficient conditions for the existence of UMPBT’s and propose a unified approach for their derivation. An important application of our methodology is the extension of UMPBT’s to testing whether the non-centrality parameter of a chi-squared distribution is zero. The resulting tests have broad applicability, providing default alternative hypotheses to compute Bayes factors in, for example, Pearson’s chi-squared test for goodness-of-fit, tests of independence in contingency tables, and likelihood ratio, score and Wald tests.


The authors would like to thank the editor-in-chief, associate editor and anonymous referees for their helpful comments that improved the presentation of the materials in this article. This work was supported by NIH grant R01CA158113.


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Amir Nikooienejad. Valen E. Johnson. "On the Existence of Uniformly Most Powerful Bayesian Tests With Application to Non-Central Chi-Squared Tests." Bayesian Anal. 16 (1) 93 - 109, March 2021.


Published: March 2021
First available in Project Euclid: 7 January 2020

MathSciNet: MR4194274
Digital Object Identifier: 10.1214/19-BA1194

Keywords: Bayesian hypothesis test , Chi-squared tests , test of independence in contingency tables , uniformly most powerful Bayesian tests

Vol.16 • No. 1 • March 2021
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