Generalized Linear Models in Vehicle Insurance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F14%3A00075556" target="_blank" >RIV/00216224:14560/14:00075556 - isvavai.cz</a>

Výsledek na webu

<a href="http://acta.mendelu.cz/62/2/0383/" target="_blank" >http://acta.mendelu.cz/62/2/0383/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/actaun201462020383" target="_blank" >10.11118/actaun201462020383</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Generalized Linear Models in Vehicle Insurance

Popis výsledku v původním jazyce

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 givenrisk 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 comparedby analysis of deviance and Akaike information criterion (AIC). Based on this comparison, the model for the best estimate of annual claim frequency is chosen.

Název v anglickém jazyce

Generalized Linear Models in Vehicle Insurance

Popis výsledku anglicky

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 givenrisk 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 comparedby analysis of deviance and Akaike information criterion (AIC). Based on this comparison, the model for the best estimate of annual claim frequency is chosen.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

—

Svazek periodika

62

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

383-388

Kód UT WoS článku

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EID výsledku v databázi Scopus

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