A new strategy for finite-sample valid prediction of future insurance claims in the regression setting
Applications
2026-01-30 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2601.21153,
title = {A new strategy for finite-sample valid prediction of future insurance claims in the regression setting},
author = {Liang Hong},
journal= {arXiv preprint arXiv:2601.21153},
year = {2026}
}