Union–intersection permutation solution for two-sample equivalence testing
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One of the well-known problems with testing for sharp null hypotheses against two-sided alternatives is that, when sample sizes diverge, every consistent test rejects the null with a probability converging to one, even when it is true. This kind of problem emerges in practically all applications of traditional two-sided tests. The main purpose of the present paper is to overcome this very intriguing impasse by considering a general solution to the problem of testing for an equivalence null interval against a two one-sided alternative. Our goal is to go beyond the limitations of likelihood-based methods by working in a nonparametric permutation framework. This solution requires the nonparameteric Combination of dependent permutation tests, which is the methodological tool that achieves Roy’s Union–intersection principle. To obtain practical solutions, the related algorithm is presented. To appreciate its effectiveness for practical purposes, a simple example and some simulation results are also presented. In addition, for every pair of consistent partial test statistics it is proved that, if sample sizes diverge, when the effect lies in the open equivalence interval, the Rejection probability (RP) converges to zero. Analogously, if the effect lies outside that interval, the RP converges to one.
KeywordsNonparametric combination Permutation tests Testing equivalence Two one-sided tests Union–intersection principle
Authors acknowledge anonymous referees and associate editor for comments which helped to improved the manuscript. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. All authors have contributed equally to the manuscript.
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