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.

Mit der vorliegenden empirischen Studie untersucht die FOM-FIRSTForschungsinitiative die Motivationsfaktoren von Unternehmen zur Erstellung von Nachhaltigkeitsberichten, indem sie den Zusammenhang zwischen den veröffentlichten Nachhaltigkeitsbemühungen deutscher Aktiengesellschaften und dem...

In this work we introduce a method for classification and visualization. In contrast to simultaneous methods like e.g. Kohonen SOM this new approach, called KMC/EDAM, runs through two stages. In the first stage the data is clustered by classical methods like K-means clustering. In the second...

We introduce the idea of Characteristic Regions to solve a classification problem. By identifying regions in which classes are dense (i.e. many observations) and also relevant (for discrimination) we can characterize the different classes. These Characteristic Regions are used to generate a...

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....

The performance of an algorithm often largely depends on some hyper parameter which should be optimized before its usage. Since most conventional optimization methods suffer from some drawbacks, we developed an alternative way to find the best hyper parameter values. Contrary to the well known...