Combining exchangeable P-values

• 成果类型: Article
• 署名作者: Gasparin, Matteo; Wang, Ruodu; Ramdas, Aaditya
• 署名单位: University of Padua; University of Waterloo; Carnegie Mellon University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14082
• 发表日期: 2025-03-18
• 关键词: random-variables; calibration; EFFICIENCY

摘要

The problem of combining P-values is an old and fundamental one, and the classic assumption of independence is often violated or unverifiable in many applications. There are many well-known rules that can combine a set of arbitrarily dependent P-values (for the same hypothesis) into a single P-value. We show that essentially all these existing rules can be strictly improved when the P-values are exchangeable, or when external randomization is allowed (or both). For example, we derive randomized and/or exchangeable improvements of well-known rules like twice the median and twice the average, as well as geometric and harmonic means. Exchangeable Pvalues are often produced one at a time (for example, under repeated tests involving data splitting), and our rules can combine them sequentially as they are produced, stopping when the combined P-values stabilize. Our work also improves rules for combining arbitrarily dependent P-values, since the latter becomes exchangeable if they are presented to the analyst in a random order. The main technical advance is to show that all existing combination rules can be obtained by calibrating the P-values to e-values (using an a-dependent calibrator), averaging those e-values, converting to a level-a test using Markov's inequality, and finally obtaining P-values by combining this family of tests; the improvements are delivered via recent randomized and exchangeable variants of Markov's inequality.