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Mathematics > Statistics Theory
Title: Safe Testing
(Submitted on 18 Jun 2019 (v1), revised 10 Jun 2020 (this version, v2), latest version 10 Mar 2023 (v5))
Abstract: We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several tests. Even in the common scenario of optional continuation, where the decision to perform a new test depends on previous test outcomes, 'safe' tests based on e-values generally preserve Type-I error guarantees. Our main result shows that e-values exist for completely general testing problems with composite null and alternatives. Their prime interpretation is in terms of gambling or investing, each e-value corresponding to a particular investment. Surprisingly, optimal 'GROW' e-values, which lead to fastest capital growth, are fully characterized by the joint information projection (JIPr) between the set of all Bayes marginal distributions on H0 and H1. Thus, optimal e-values also have an interpretation as Bayes factors, with priors given by the JIPr. We illustrate the theory using several 'classic' examples including a one-sample safe t-test and the 2 x 2 contingency table. Sharing Fisherian, Neymanian and Jeffreys-Bayesian interpretations, e-values and safe tests may provide a methodology acceptable to adherents of all three schools.
Submission history
From: Rianne de Heide [view email][v1] Tue, 18 Jun 2019 20:39:27 GMT (85kb,D)
[v2] Wed, 10 Jun 2020 08:38:35 GMT (96kb,D)
[v3] Mon, 6 Dec 2021 20:41:47 GMT (75kb,D)
[v4] Tue, 7 Mar 2023 15:17:24 GMT (165kb,D)
[v5] Fri, 10 Mar 2023 13:14:45 GMT (165kb,D)
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