An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount

Risks

Himchan Jeong¹  George Tzougas² 

[1] Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada;Department of Statistics, London School of Economics and Political Science, London WC2A 2AE, UK;

关键词: Exponential–Generalized Inverse Gaussian Distribution;    EM Algorithm;    regression models for the mean, dispersion and shape parameters;    non-life insurance;    heavy-tailed losses;   

DOI  :  10.3390/risks9010019

来源: DOAJ

【 摘 要 】

This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of flexibility to fit moderate costs with high frequencies and heavy-tailed claim sizes, as they both represent significant proportions of the total loss in non-life insurance. The model’s implementation is illustrated by a real data application which involves fitting claim size data from a European motor insurer. The maximum likelihood estimation of the model parameters is achieved through a novel Expectation Maximization (EM)-type algorithm that is computationally tractable and is demonstrated to perform satisfactorily.

【 授权许可】

Unknown