Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22562
Authors: 
Marin-Galiano, Marcos
Christmann, Andreas
Year of Publication: 
2004
Series/Report no.: 
Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2004,49
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.
Subjects: 
Claim size
insurance tariff
logistic regression
statistical machine learning
support vector regression
Document Type: 
Working Paper

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