发表机构
ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文系统化了一般保险精算中的模型验证工具，区分保单加权与风险暴露加权度量，强调校准尺度对保费方案的关键性。
AI 中文摘要
本手稿正式化了一般保险精算建模中最常用的模型验证工具。这些工具包括图形工具，如校准图、实际值与期望值对比图、提升图、墨菲图，以及经典统计工具，如布雷格曼损失、偏差损失、基本损失、墨菲分解和基尼分数。特别强调了校准和判别是在保单加权还是风险暴露加权的人口度量下进行研究。这一区别对于确保保费方案在正确的尺度上校准至关重要。
英文摘要
This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling. These include graphical tools like calibration plots, actual-vs-expected plots, lift charts, Murphy diagrams, as well as classical statistical tools such as Bregman losses, deviance losses, elementary losses, Murphy's decomposition and Gini scores. Particular emphasis is placed on whether calibration and discrimination are studied under a policy-weighted or an exposure-weighted population measure. This distinction is crucial in ensuring that premium schemes are calibrated on the correct scale.
