Insurance: an R-Program to Model Insurance Data

Marcos Marin-Galiano and Andreas Christmann

No 2004,49, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

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.

Keywords: Claim size; insurance tariff; logistic regression; statistical machine learning; support vector regression (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/22562/1/tr49-04.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:200449

Access Statistics for this paper

More papers in Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().