基于半连续两部模型的保险损失预测  

Prediction of insurance loss based on semicontinuous two-part model

作　　者：鲁亚会 刘爱义 LU Yahui;LIU Aiyi(School of Economics and Management,Zhejiang University of Science and Technology,Hangzhou 310023,Zhejiang,China;National Institutes of Health,Bethesda 20817,Maryland,USA)

机构地区：[1]浙江科技学院经济与管理学院,杭州310023 [2]美国国立卫生研究院,美国贝塞斯达20817

出　　处：《浙江科技学院学报》2023年第6期467-474,共8页Journal of Zhejiang University of Science and Technology

基　　金：杭州市哲学社会科学规划课题(Z23JC042);国家自然科学基金项目(11971433)。

摘　　要：【目的】提高保险领域中保单累积损失预测的准确率。传统的Tweedie回归模型只能对非零均值建立回归模型,却不能对零概率建立回归模型,从而导致该模型的拟合效果并不理想。【方法】考虑到保单损失数据中往往包含着大量的零索赔,此时可视其为一种半连续型数据。因此,基于半连续两部模型,并考虑到累积损失中非零连续部分的分布类型,提出3种不同的累积损失预测模型,并结合一组实际损失数据进行模型对比分析。【结果】与Tweedie回归模型相比,本研究所提出的半连续两部回归模型的赤池信息准则值(Akaike information criterion,AIC)和贝叶斯信息量准则值(Bayesian information criterion,BIC)更小,具有较好的拟合效果。【结论】本研究结果可为保险领域中的保单累积损失预测提供参考。[Objective]It is imperative to improve the prediction accuracy of policy accumulated loss in the insurance field.The traditional Tweedie regression model can only establish a regression model for non-zero mean value,but not for zero probability,so the fitting effect of the model is not ideal.[Method]Considering that the policy loss data often contains a large number of zero claims,it can be regarded as semicontinuous data.Therefore,based on the semicontinuous two-part model and considering the distribution type of non-zero continuous part of accumulated loss,three different accumulated loss prediction models were proposed,and a set of actual loss data was combined to make a comparative analysis of the models.[Result]The results show that,compared with the Tweedie regression model the AIC(Akaike information criterion)and BIC(Bayesian information criterion)values of the two semicontinuous regression models are smaller,which have better fitling effects.[Conclusion]The results can provide a reference for the prediction of policy accumulated loss in the insurance field.

关 键 词：累积损失预测 半连续数据 Tweedie回归模型 两部回归模型 

分 类 号：F842[经济管理—保险] O212.1[理学—概率论与数理统计]