On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data
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.
KeywordsData mining kernel logistic regression robustness statistical machine learning support vector regression
2000 MR Subject Classification62G08 62G35 62G32
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- 1.Bartlett, P.L., Tewari, A. Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. Preprint, University of California, Berkeley, 2004Google Scholar
- 6.Christmann, A., Steinwart, I. On robust properties of convex risk minimization methods for pattern recognition. Journal of Machine Learning Research, 5: 1007–1034 (2004a)Google Scholar
- 7.Christmann, A., Steinwart, I. Consistency and robustness of kernel based regression. University of Dortmund, SFB-475, TR-01/05 Submitted, 2005Google Scholar
- 8.Embrechts, P., Klüppelberg, C., Mikosch, T. Modelling Extreme Events for Insurance and Finance. Springer-Verlag, Berlin, 1997Google Scholar
- 10.Keerthi, S.S., Duan, K., Shevade, S.K., Poo, A.N. A fast dual algorithm for kernel logistic regression. In Machine Learning: Proceedings of the Ninetheenth International Conference, Kaufmann, San Francisco, 299–306 2004Google Scholar
- 11.Leisch, F. et al. R package e1071. http://cran.r-project.org, 2003Google Scholar
- 12.Nelder, J.A., Mead, R. A simplex algorithm for function minimization. Computer Journal, 7: 308–313 (1965)Google Scholar
- 14.Rüping, S. myKLR - kernel logistic regression. Department of Computer Science, University of Dortmund. http://www-ai.cs.uni-dortmund.de/SOFTWARE, 2003Google Scholar
- 15.Schölkopf, B., Smola, A. Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, Massachusetts, 2002 Google Scholar
- 17.Vapnik, V. Statistical Learning Theory. John Wiley & Sons, New York, 1998Google Scholar