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

Author: Christmann, Andreas

Source: Acta Mathematicae Applicatae Sinica, Volume 21, Number 2, May 2005 , pp. 193-208(16)

Publisher: Springer

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Abstract:

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.

Keywords: Data mining; kernel logistic regression; robustness; statistical machine learning; support vector regression; 62G08; 62G35; 62G32

Document Type: Research article

DOI: http://dx.doi.org/10.1007/s10255-005-0229-8

Affiliations: 1: Email: christmann@statistik.uni-dortmund.de

Publication date: 2005-05-01