Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/258109 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 1 [Article No.:] 19 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-17
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
dispersion and shape parameters
EM Algorithm
Exponential-Generalized Inverse Gaussian Distribution
heavy-tailed losses
non-life insurance
regression models for the mean
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Erscheint in der Sammlung:

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.