A new strategy for finite-sample valid prediction of future insurance claims in the regression setting

TL;DR

This paper introduces a novel method that transforms unsupervised iid predictive techniques into regression prediction methods, allowing actuaries to generate numerous finite-sample valid prediction intervals for future insurance claims.

Contribution

It presents a new strategy for finite-sample valid prediction intervals in insurance regression, bridging the gap between unsupervised iid methods and regression prediction.

Findings

Enables actuaries to produce infinitely many valid prediction intervals.

Provides a practical approach for finite-sample prediction in insurance.

Addresses a key gap in insurance literature for regression prediction.

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.