A new and flexible class of sharp asymptotic time-uniform confidence sequences
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909649687543808 |
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| author | Gnettner, Felix Kirch, Claudia |
| author_facet | Gnettner, Felix Kirch, Claudia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10380 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A new and flexible class of sharp asymptotic time-uniform confidence sequences Gnettner, Felix Kirch, Claudia Statistics Theory Methodology Machine Learning 62G15 (Primary) 62G10, 62G20, 62L10 (Secondary) 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. |
| title | A new and flexible class of sharp asymptotic time-uniform confidence sequences |
| topic | Statistics Theory Methodology Machine Learning 62G15 (Primary) 62G10, 62G20, 62L10 (Secondary) |
| url | https://arxiv.org/abs/2502.10380 |