Loss data structures in non-life insurance
businesses are increasingly complex, and the tendency of correlation and
heterogeneity is gradually presented. Hierarchical model can breakthrough limitation
that the traditional rate determination method only analyzes the loss data of
the same insurance policy; meanwhile, the accuracy of complex structure data prediction is improved. This
paper, using a hierarchical generalized linear model, studies the non-life rate determination of
multi-year loss data and takes auto insurance data for empirical analysis. The
research results show that GLMM’s fitting degree is greatly improved compared
with GLM, considering the random effects. It can more effectively reflect
different risk individual differences and also reveal the heterogeneity and
correlation of risk individual loss during multiple insurance periods.
Górecki, J., Hofert, M. and Holeňa, M. (2016) An Approach to Structure Determination and Estimation of Hierarchical Archimedean Copulas and Its Application to Bayesian Classification. Journal of Intelligent Information Systems, 46, 21-59.
Li, H. and Duan, P.J. (2016) The Promotion of the Relationship between the Instantaneous Compensation of Death and the Present Value Model of Death Insurance Actuarial Calculation Based on the Age of the Score Age. Journal of Jiamusi University, 34, 144-146.