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.
View on arXiv@article{gnettner2025_2502.10380,
title={ A new and flexible class of sharp asymptotic time-uniform confidence sequences },
author={ Felix Gnettner and Claudia Kirch },
journal={arXiv preprint arXiv:2502.10380},
year={ 2025 }
}