Biometrika
Nov 03, 2026
Optimal e-values for testing the mean of a bounded random variable against a composite alternative
Authors
S Arnold
E Clerico
Research Topics
Machine Learning
Hypothesis Testing
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asag070 -
Published:
November 03, 2026 -
Added to Tracker:
Nov 03, 2026
Abstract
Abstract We derive explicitly the e-values with optimal (relative) growth rate in the worst case for testing the mean of a bounded random variable, thereby providing the first application of the (RE)GROW quality criteria beyond the assumption of mutually absolutely continuous hypotheses for e-values originally proposed by Grünwald et al. (2024). For both criteria, we explicitly characterise the alternatives that are most difficult to test against and show that they admit a meaningful interpretation. We give two important examples in which REGROW provides a powerful quality criterion to choose optimal e-variables whereas GROW leads to trivial solutions.
Author Details
S Arnold
AuthorE Clerico
AuthorResearch Topics & Keywords
Machine Learning
Research AreaHypothesis Testing
Research AreaCitation Information
APA Format
S Arnold
&
E Clerico
(2026)
.
Optimal e-values for testing the mean of a bounded random variable against a composite alternative.
Biometrika
, 10.1093/biomet/asag070.
BibTeX Format
@article{paper1840,
title = { Optimal e-values for testing the mean of a bounded random variable against a composite alternative },
author = {
S Arnold
and E Clerico
},
journal = { Biometrika },
year = { 2026 },
doi = { 10.1093/biomet/asag070 },
url = { https://doi.org/10.1093/biomet/asag070 }
}