On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data

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DC Field	Value	Language
dc.contributor.author	Christmann, Andreas	de
dc.date.accessioned	2004-12-06T18:38:50Z	-
dc.date.available	2004-12-06T18:38:50Z	-
dc.date.issued	2004	de
dc.identifier.uri	http://hdl.handle.net/2003/4872	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15069	-
dc.description.abstract	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 e-support vector regression.	en
dc.format.extent	2783551 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	data mining	en
dc.subject	insurance tariffs	en
dc.subject	kernel logistic regression	en
dc.subject	machine learning	en
dc.subject	regression	en
dc.subject	robustness	en
dc.subject	simplicity	en
dc.subject	support vector machine	en
dc.subject	support vector regression	en
dc.subject.ddc	310	de
dc.title	On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 475

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