A new strategy for finite-sample valid prediction of future insurance claims in the regression setting
This addresses a specific gap in insurance literature for actuaries, but appears incremental as it adapts existing methods to a new setting.
The paper tackles the lack of finite-sample valid prediction intervals for future insurance claims in regression settings by proposing a strategy that converts unsupervised i.i.d. predictive methods to regression methods, enabling actuaries to generate infinitely many such intervals.
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.