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arXiv · 1912.06116

E-values: Calibration, combination, and applications

Vladimir Vovk·Ruodu Wang
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Abstract

Multiple testing of a single hypothesis and testing multiple hypotheses are usually done in terms of p-values. In this paper we replace p-values with their natural competitor, e-values, which are closely related to betting, Bayes factors, and likelihood ratios. We demonstrate that e-values are often mathematically more tractable; in particular, in multiple testing of a single hypothesis, e-values can be merged simply by averaging them. This allows us to develop efficient procedures using e-values for testing multiple hypotheses.

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Vladimir Vovk, Ruodu Wang. 2019-12-12. E-values: Calibration, combination, and applications. https://doi.org/10.1214/20-aos2020

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