{"ID":3083630,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T03:54:17.966829144Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06332","arxiv_id":"2606.06332","title":"Bentkus-type asymptotic e-values","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.06332","url_pdf":"https://arxiv.org/pdf/2606.06332v1","authors":"[\"Diego Martinez-Taboada\",\"Ben Chugg\",\"Aaditya Ramdas\"]","published":"2026-06-04T16:08:08Z","proceeding":"math.ST","tasks":"[\"math.ST\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}