Abstract: In car insurance business, to predict the claim frequency become a focus on the research of theory and application in non-life actuarial studies. However, the traditional models and estimation methods can not be applied under the case of high-dimensional information (covariates). Hence, in this paper, based on the mean count model, we first use convex penalty function for variable selection, and then, non-parameter method is employed to get an estimate of the unspecified baseline function. A small simulation and a real data analysis are given as well to assess the proposed model the methods in this paper.

摘要： 汽车保险业务中,其索赔频数预测模型一直以来都是非寿险精算理论和应用研究的重点。但是，在含有高维附加信息(协变量)的情形下，传统的估计方法就不再适用。本文在均值计数模型基础上,首先利用凸惩罚函数进行变量选择,找到影响车险索赔频数的显著性因子，最后通过模拟和实例分析来评价该模型和方法的可行性。