Many robust statistical procedures have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets. Secondly, robust confidence intervals for the estimated parameters or robust predictions according to the fitted models are often unknown. Here, we...

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...

Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust support vector machine classifiers under feature data...

Auf Grundlagen der technischen Finanzmarktanalyse wird ein Algorithmus für eine sicherheitsorientierte Wertpapierhandelsstrategie entwickelt. Maßgeblich für den Erfolg der Handelsstrategie ist dabei eine möglichst optimale Gewichtung mehrerer Indikatoren. Die Ermittlung dieser Gewichte...