Acta Mathematicae Applicatae Sinica

, Volume 21, Issue 2, pp 193–208 | Cite as

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

Original Papers


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. We use a nonparametric approach based on a combination of kernel logistic regression and ε-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.


Data mining kernel logistic regression robustness statistical machine learning support vector regression 

2000 MR Subject Classification

62G08 62G35 62G32 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of DortmundDortmundGermany

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