Full metadata record
DC FieldValueLanguage
dc.contributor.authorChristmann, Andreasde
dc.contributor.authorMarin-Galiano, Marcosde
dc.date.accessioned2004-12-06T18:51:24Z-
dc.date.available2004-12-06T18:51:24Z-
dc.date.issued2004de
dc.identifier.urihttp://hdl.handle.net/2003/5301-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6724-
dc.description.abstractData 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.extent181435 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.subjectclaim sizeen
dc.subjectinsurance tariffen
dc.subjectlogistic regressionen
dc.subjectstatistical machine learningen
dc.subjectsupport vector regressionen
dc.subject.ddc310de
dc.titleInsurance: an R-Program to Model Insurance Dataen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
49_04.pdfDNB177.18 kBAdobe PDFView/Open


This item is protected by original copyright



This item is protected by original copyright rightsstatements.org