On a strategy to develop robust and simple tariffs from motor vehicle insurance data

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression.

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Year of publication:	2004
Authors:	Christmann, Andreas
Publisher:	Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Subject:	Kraftfahrtversicherung \| Versicherungsprämie \| Regression \| Theorie \| Data Mining \| Classification \| Insurance tariffs \| Kernel logistic regression \| Machine learning \| Robustness \| Simplicity \| Support Vector Machine \| Support Vector Regression

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Series:	Technical Report ; 2004,16
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	824595432 [GVK] hdl:10419/49326 [Handle] RePEc:zbw:sfb475:200416 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010306241