Department of Mathematics and Statistics
|Thursday, October 12, 2023
|1:15pm – 2:15pm
|Baozhen Wang, Binghamton University
|E-values: Calibration, combination and applications
Multiple testing of a single hypothesis and testing multiple hypotheses are usually done in terms of p-values. In this paper, the authors replace p-values with their natural competitor, e-values, which are closely related to betting, Bayes factors and likelihood ratios. They 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.