Bentkus-type asymptotic e-values
Diego Martinez-Taboada Ben Chugg Aaditya Ramdas
作者信息
Abstract
Asymptotic e-values are emerging as a powerful alternative to asymptotic p-values, particularly in post-hoc inference and multiple testing, where significance levels may be data-dependent. Existing asymptotic e-values, however, suffer from the ``missing factor,'' a scaling inefficiency resulting in overly conservative inference. Drawing on the framework of near-optimal concentration inequalities developed by Bentkus in the 2000s, we introduce Bentkus-type asymptotic e-values and prove that they successfully eliminate the missing factor. We also demonstrate both theoretically and empirically that Bentkus-type e-values consistently deliver sharper inference than existing alternatives, leading to tighter post-hoc confidence intervals and higher rejection rates in multiple testing procedures.引用本文复制引用
Diego Martinez-Taboada,Ben Chugg,Aaditya Ramdas.Bentkus-type asymptotic e-values[EB/OL].(2026-06-04)[2026-06-09].https://arxiv.org/abs/2606.06332.学科分类
数学
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