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. Entropy 2015, 17 6535
When performing simultaneous hypotheses testing is expected that the decisions obtained therein are logically consistent with each other. In this work, we find restrictions under which simultaneous Bayes tests meet logical conditions separately or jointly. It is shown that the conditions for the simultaneous tests meet these conditions alone are quite intuitive. However, when trying to obey the conditions jointly, we lose optimality. Furthermore, we evaluate the relationship between these tests and simultaneous Bayes tests generated by estimators, ie, we show that, under some conditions, to choose an estimator based on Bayes decision is equivalent to choosing a decision based on a Bayes test. Finally, we show that if we take a decision based on Maximum Likelihood Estimators, then that decision should be equal to taking a Bayes test and concluded that these decisions are admissible and obey the Likelihood Principle.
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