This paper discusses whether differences in the data structure of observational and experimental studies should lead to different strategies for variable selection. On the one hand, it is argued that outliers in the predictor variables have to be treated differently in the two kinds of studies....

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...

In this paper business cycles are considered as a multivariate phenomenon and not as a univariate one determined e.g. by the GNP. The subject is to look for the number of phases of a business cycle, which can be motivated by the number of clusters in a given dataset of macro-economic variables....

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...

Time series analysis and multivariate control charts are used to devise a real-time monitoring strategy in a drilling process. The process is used to produce holes with high length-to-diameter ratio, good surface finish and straightness. It is subject to dynamic disturbances that are classified...