E-values: Calibration, combination, and applications
Statistics Theory
2021-10-26 v4 Statistics Theory
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.
Cite
@article{arxiv.1912.06116,
title = {E-values: Calibration, combination, and applications},
author = {Vladimir Vovk and Ruodu Wang},
journal= {arXiv preprint arXiv:1912.06116},
year = {2021}
}
Comments
48 pages, 5 figures, 4 algorithms. A new title and improved presentation