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Research PaperResearchia:202604.06029

Power one sequential tests exist for weakly compact $\mathscr P$ against $\mathscr P^c$

Ashwin Ram

Abstract

Suppose we observe data from a distribution $P$ and we wish to test the composite null hypothesis that $P\in\mathscr P$ against a composite alternative $P\in \mathscr Q\subseteq \mathscr P^c$. Herbert Robbins and coauthors pointed out around 1970 that, while no batch test can have a level $α\in(0,1)$ and power equal to one, sequential tests can be constructed with this fantastic property. Since then, and especially in the last decade, a plethora of sequential tests have been developed for a wide...

Submitted: April 6, 2026Subjects: Statistics; Data Science

Description / Details

Suppose we observe data from a distribution PP and we wish to test the composite null hypothesis that PPP\in\mathscr P against a composite alternative PQPcP\in \mathscr Q\subseteq \mathscr P^c. Herbert Robbins and coauthors pointed out around 1970 that, while no batch test can have a level α(0,1)α\in(0,1) and power equal to one, sequential tests can be constructed with this fantastic property. Since then, and especially in the last decade, a plethora of sequential tests have been developed for a wide variety of settings. However, the literature has not yet provided a clean and general answer as to when such power-one sequential tests exist. This paper provides a remarkably general sufficient condition (that we also prove is not necessary). Focusing on i.i.d. laws in Polish spaces without any further restriction, we show that there exists a level-αα sequential test for any weakly compact P\mathscr P, that is power-one against Pc\mathscr P^c (or any subset thereof). We show how to aggregate such tests into an ee-process for P\mathscr P that increases to infinity under Pc\mathscr P^c. We conclude by building an ee-process that is asymptotically relatively growth rate optimal against Pc\mathscr P^c, an extremely powerful result.


Source: arXiv:2604.03218v1 - http://arxiv.org/abs/2604.03218v1 PDF: https://arxiv.org/pdf/2604.03218v1 Original Link: http://arxiv.org/abs/2604.03218v1

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Date:
Apr 6, 2026
Topic:
Data Science
Area:
Statistics
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