Sequential Monte Carlo testing by betting

• 成果类型: Article
• 署名作者: Fischer, Lasse; Ramdas, Aaditya
• 署名单位: University of Bremen; Carnegie Mellon University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkaf014
• 发表日期: 2025
• 页码: 1200-1220
• 关键词: rerandomization; implementation; balance

摘要

In a Monte Carlo test, the observed dataset is fixed, and several resampled or permuted versions of the dataset are generated in order to test a null hypothesis that the original dataset is exchangeable with the resampled/permuted ones. Sequential Monte Carlo tests aim to save computational resources by generating these additional datasets sequentially one by one and potentially stopping early. While earlier tests yield valid inference at a particular prespecified stopping rule, our work develops a new anytime-valid Monte Carlo test that can be continuously monitored, yielding a p-value or e-value at any stopping time possibly not specified in advance. It generalizes the well-known method by Besag and Clifford, allowing it to stop at any time, but also encompasses new sequential Monte Carlo tests that tend to stop sooner under the null and alternative without compromising power. The core technical advance is the development of new test martingales for testing exchangeability against a very particular alternative based on a testing by betting technique. The proposed betting strategies are guided by the derivation of a simple log-optimal betting strategy, have closed-form expressions for the wealth process, provable guarantees on resampling risk, and display excellent power in practice.