We propose a new information theoretically based optimization criterion for the estimation of mixture density models and compare it with other methods based on maximum likelihood and maximum a posterio estimation. For the optimization, we employ an evolutionary algorithm which estimates both...

We use Dynamic Bayesian networks to classify business cycle phases. We compare classifiers generated by learning the Dynamic Bayesian network structure on different sets of admissible network structures. Included are sets of network structures of the Tree Augmented Naive Bayes (TAN) classifiers...

This paper presents a model of resource extraction based on two of its key aspects: investment and taxation. A mining firm is considered which may be in an environment of perfect competition or have a monopoly. It plans to extract from a fixed stock of resources. Before extraction begins, the...

The Department of Statistics at the University of Dortmund, established in 1973, currently comprises of about 500 students, 10 professors and about 50 scientific co-workers. In the meantime more than 1000 diplomas in Statistics are awarded. The analysis at hand focuses on three aspects: the...

In recent years in the fields of statistics and machine learning an increasing amount of so called local classification methods has been developed. Local approaches to classification are not new, but have lately become popular. Well-known examples are the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$k$$</EquationSource> </InlineEquation> nearest neighbors method and...</equationsource></inlineequation>