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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Veröffentlicht in:	Risks (Basel) Jg. 9; H. 1; S. 1 - 17
Hauptverfasser:	Tzougas, George, Jeong, Himchan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI 01.01.2021 MDPI AG
Schlagworte:	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
Online-Zugang:	Volltext
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