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