Over-Dispersed Claim Counts Regression Models and Their Applications in Auto Insurance

摘要：

Poisson regression model is widely used in auto claim count models for ratemaking practice. But as the actual claim count data often appears to be over-dispersed (i.e. variance is larger than mean), the Poisson regression model may not produce reasonable results. The paper fist compares several mixed Poisson regression models that may be used to account for overdispersion in claim count data, including Negative Binomial regression (NB), Poisson-inverse Gaussian regression (PIG), Poisson-Lognormal regression (PLN), generalized Poisson regression (GP). Then the paper proposes two new regression models that may also be used for over-dispersed claim count data, namely mixed binomial regression (MB) and mixed negative binomial regression (MNB). Finally the paper applies all these models to an actual auto claim count data and compares the results.

关键词： Auto Insurance Claim Count Over-dispersion Mized Binomial Mized Negative Binomial

作者: MENG Shengwang

作者单位: School of Statistics, Renmin University of China, Beijing, P.R.China, 100872

会议类型: 国际会议

会议名称: 2009 International Institute of Applied Statistics Studies(2009 国际应用统计学术研讨会)

会议地点: 青岛

会议语种:英文

页码: 1-8

在线出版日期: 2009-07-25（万方平台首次上网日期，不代表论文的发表时间）