E-VALUES: CALIBRATION, COMBINATION AND APPLICATIONS
成果类型：
Article
署名作者：
Vovk, Vladimir; Wang, Ruodu
署名单位：
University of London; Royal Holloway University London; University of Waterloo
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/20-AOS2020
发表日期：
2021
页码：
1736-1754
关键词：
摘要：
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.