An expectation-maximization algorithm for the exponential-generalized inverse Gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount

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...

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Vydané v:Risks (Basel) Ročník 9; číslo 1; s. 1 - 17
Hlavní autori: Tzougas, George, Jeong, Himchan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI 01.01.2021
MDPI AG
Predmet:
Algorithms
Behavior
dispersion and shape parameters
EM Algorithm
Exponential-Generalized Inverse Gaussian Distribution
heavy-tailed losses
Insurance premiums
Life insurance
non-life insurance
Random variables
regression models for the mean
regression models for the mean, dispersion and shape parameters
Reinsurance
ISSN:2227-9091, 2227-9091
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-9091
2227-9091
DOI:10.3390/risks9010019