Insurance: an R-Program to Model Insurance Data

dc.contributor.author	Christmann, Andreas	de
dc.contributor.author	Marin-Galiano, Marcos	de
dc.date.accessioned	2004-12-06T18:51:24Z
dc.date.available	2004-12-06T18:51:24Z
dc.date.issued	2004	de
dc.description.abstract	Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie’s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and epsilon-support vector regression from modern statistical machine learning.	en
dc.format.extent	181435 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/5301
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6724
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.subject	claim size	en
dc.subject	insurance tariff	en
dc.subject	logistic regression	en
dc.subject	statistical machine learning	en
dc.subject	support vector regression	en
dc.subject.ddc	310	de
dc.title	Insurance: an R-Program to Model Insurance Data	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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