A new strategy for finite-sample valid prediction of future insurance claims in the regression setting
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866912857918013440 |
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| author | Hong, Liang |
| author_facet | Hong, Liang |
| contents | The extant insurance literature demonstrates a paucity of finite-sample valid prediction intervals of future insurance claims in the regression setting. To address this challenge, this article proposes a new strategy that converts a predictive method in the unsupervised iid (independent identically distributed) setting to a predictive method in the regression setting. In particular, it enables an actuary to obtain infinitely many finite-sample valid prediction intervals in the regression setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21153 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A new strategy for finite-sample valid prediction of future insurance claims in the regression setting Hong, Liang Applications Machine Learning 62P05, 91G05 The extant insurance literature demonstrates a paucity of finite-sample valid prediction intervals of future insurance claims in the regression setting. To address this challenge, this article proposes a new strategy that converts a predictive method in the unsupervised iid (independent identically distributed) setting to a predictive method in the regression setting. In particular, it enables an actuary to obtain infinitely many finite-sample valid prediction intervals in the regression setting. |
| title | A new strategy for finite-sample valid prediction of future insurance claims in the regression setting |
| topic | Applications Machine Learning 62P05, 91G05 |
| url | https://arxiv.org/abs/2601.21153 |