KAFKOVÁ, Silvie and Lenka KŘIVÁNKOVÁ. Generalized Linear Models in Vehicle Insurance. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. Mendelova univerzita v Brně, 2014, vol. 62, No 2, p. 383-388. ISSN 1211-8516. Available from: https://dx.doi.org/10.11118/actaun201462020383.
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Basic information
Original name Generalized Linear Models in Vehicle Insurance
Authors KAFKOVÁ, Silvie (203 Czech Republic, guarantor, belonging to the institution) and Lenka KŘIVÁNKOVÁ (203 Czech Republic, belonging to the institution).
Edition Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendelova univerzita v Brně, 2014, 1211-8516.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 50600 5.6 Political science
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/00216224:14560/14:00075556
Organization unit Faculty of Economics and Administration
Doi http://dx.doi.org/10.11118/actaun201462020383
Keywords in English vehicle insurance; generalized linear model; poisson distribution; link function; analysis of deviance; Akaike information criterion
Tags International impact, Reviewed
Changed by Changed by: Mgr. et Mgr. Nikol Zachovalová Barochová, učo 179010. Changed: 31/3/2015 13:36.
Abstract
Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver. In this paper, an analysis of the portfolio of vehicle insurance data using a generalized linear model (GLM) is performed. The main advantage of the approach presented in this article is that the GLMs are not limited by inflexible preconditions. Our aim is to predict the relation of annual claim frequency on given risk factors. Based on a large real-world sample of data from 57 410 vehicles, the present study proposed a classification analysis approach that addresses the selection of predictor variables. The models with different predictor variables are compared by analysis of deviance and Akaike information criterion (AIC). Based on this comparison, the model for the best estimate of annual claim frequency is chosen. All statistical calculations are computed in R environment, which contains stats package with the function for the estimation of parameters of GLM and the function for analysis of deviation.
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