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

Autor:	George Tzougas, Himchan Jeong
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Exponential–Generalized Inverse Gaussian Distribution EM Algorithm regression models for the mean dispersion and shape parameters non-life insurance heavy-tailed losses Insurance HG8011-9999
Zdroj:	Risks, Vol 9, Iss 1, p 19 (2021)
Druh dokumentu:	article
ISSN:	2227-9091
DOI:	10.3390/risks9010019
Popis:	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.
Databáze:	Directory of Open Access Journals
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