报告题目：A comparative analysis of several multivariate zero-inflated and zero-modified models with applications in insurance
时 间：2024年11月15日（星期五）16：00
地 点：科研楼18号楼1102
主 办：数学与统计学院、分析数学及应用教育部重点实验室、福建省分析数学及应用重点实验室、福建省应用数学中心（福建师范大学）、福建师范大学数学研究中心
参加对象：感兴趣的老师和学生
报告摘要：Claim frequency data in insurance records the number of claims on insurance policies during a finite period of time. Given that insurance companies operate with multiple lines of insurance business where the claim frequencies on different lines of business are often correlated, multivariate count modeling with dependence for claim frequency is therefore essential. Due in part to the operation of bonus-malus systems, claims data in automobile insurance are often characterized by an excess of common zeros. This feature is referred to as multivariate zero-inflation. In this paper, we establish two ways of dealing with this feature. The first is to use a multivariate zero-inflated model, where we artificially augment the probability of common zeros based on standard multivariate count distributions. The other is to apply a multivariate zero-modified model, which deals with the common zeros and the number of claims incurred in each line given that at least one claim occurs separately. A comprehensive comparative analysis of several models under these two frameworks is conducted using the data of an automobile insurance portfolio from a major insurance company in Spain. A less common situation in insurance is the absence of some common zeros resulting from incomplete records. This feature of these data is known as multivariate zero-deflation. In this case, our proposed multivariate zero-modified model still works, as shown by the second empirical study.
报告人简介：吴学渊，副教授、博士生导师，现就职于澳大利亚墨尔本大学经济及工商管理学院精算研究中心，同时担任学院精算博士点项目主管。澳大利亚精算师协会准精算师，具有超过18年国际知名学府教学经验。瑞士洛桑大学、香港大学和南开大学访问学者。美国《数学评论》评论员及27个国际学术期刊审稿人。已发表精算相关学术论文近40篇。主要研究领域为风险理论、精算统计和机器学习等。