The optimization of the hyper-parameters of a statistical procedure or machine learning task is a crucial step for obtaining a minimal error. Unfortunately, the optimization of hyper-parameters usually requires many runs of the procedure and hence is very costly. A more detailed knowledge of the...

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. The strategy is applied...

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank....

In this paper we describe a computer intensive method to find the ridge parameter in a prediction oriented linear model. With the help of a factorial experimental design the method is tested and compared to a classical one.

We investigate properties of kernel based regression (KBR) methods which are inspired by the convex risk minimization method of support vector machines. We first describe the relation between the used loss function of the KBR method and the tail of the response variable Y . We then establish a...