A new and flexible class of sharp asymptotic time-uniform confidence sequences

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2025-06-07 DOI:10.1016/j.spl.2025.110462

Felix Gnettner, Claudia Kirch

引用次数: 0

Abstract

Confidence sequences are anytime-valid analogues of classical confidence intervals that do not suffer from multiplicity issues under optional continuation of the data collection. As in classical statistics, asymptotic confidence sequences are a nonparametric tool showing under which high-level assumptions asymptotic coverage is achieved so that they also give a certain robustness guarantee against distributional deviations. In this paper, we propose a new flexible class of confidence sequences yielding sharp asymptotic time-uniform confidence sequences under mild assumptions. Furthermore, we highlight the connection to corresponding sequential testing problems and detail the underlying limit theorem.

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一类新的灵活的尖锐渐近时一致置信序列

置信序列是经典置信区间的任意有效的类似物，在数据收集的可选延续下不会受到多重性问题的影响。与经典统计学一样，渐近置信序列是一种非参数工具，表明在该工具下可以实现高级假设的渐近覆盖，从而对分布偏差提供一定的鲁棒性保证。本文提出了一类新的柔性置信序列，在温和的假设条件下，得到了尖锐渐近时一致置信序列。此外，我们强调了与相应的序列测试问题的联系，并详细说明了潜在的极限定理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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