A Bayesian Decision-Theoretic Approach to Logically-Consistent Hypothesis Testing

Entropy

Gustavo Miranda da Silva¹  Luis Gustavo Esteves¹  Victor Fossaluza¹  Rafael Izbicki²  Sergio Wechsler¹ 

[1] Institute of Mathematics and Statistics, University of São Paulo, São Paulo, 05508-090, Brazil; E-Mails:;Department of Statistics, Federal University of São Carlos, São Carlos, 13565-905, Brazil; E-Mail:

关键词: Bayes tests;    decision theory;    logical consistency;    loss functions;    multiple hypothesis testing;   

DOI  :  10.3390/e17106534

来源: mdpi

【 摘 要 】

This work addresses an important issue regarding the performance of simultaneous test procedures: the construction of multiple tests that at the same time are optimal from a statistical perspective and that also yield logically-consistent results that are easy to communicate to practitioners of statistical methods. For instance, if hypothesis A implies hypothesis B, is it possible to create optimal testing procedures that reject A whenever they reject B? Unfortunately, several standard testing procedures fail in having such logical consistency. Although this has been deeply investigated under a frequentist perspective, the literature lacks analyses under a Bayesian paradigm. In this work, we contribute to the discussion by investigating three rational relationships under a Bayesian decision-theoretic standpoint: coherence, invertibility and union consonance. We characterize and illustrate through simple examples optimal Bayes tests that fulfill each of these requisites separately. We also explore how far one can go by putting these requirements together. We show that although fairly intuitive tests satisfy both coherence and invertibility, no Bayesian testing scheme meets the desiderata as a whole, strengthening the understanding that logical consistency cannot be combined with statistical optimality in general. Finally, we associate Bayesian hypothesis testing with Bayes point estimation procedures. We prove the performance of logically-consistent hypothesis testing by means of a Bayes point estimator to be optimal only under very restrictive conditions.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.