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....

In modern intensive care physiological variables of the critically ill can be reported online by clinical information systems. Intelligent alarm systems are needed for a suitable bedside decision support. The existing alarm systems based on fixed treshholds produce a great number of false...

Recent results on so-called SEMIFAR models introduced by Beran (1997) are discussed. The nonparametric deterministic trend is estimated by a kernel method. The differencing and fractional differencing parameters as well as the autoregressive coefficients are estimated by an approximate maximum...

The aim of this paper is to define and investigate outlier-proneness for multivariate distributions. This is done by using a concept of ordering multivariate data based on isobar-surfaces, which yields an utmost analogy of the results to the univariate case.

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data_ i.e. estimating distributional parameters and then to predict the phenomenon under study at unobserved sites within the corresponding sampling...

In discrimination tests, two different questions usually arise: First of all, we are interested in deciding whether or not there are product differences at all that might be perceived by the assessors. However, often this is not our most important concern, since the main question is whether or...

Evaluation of hazards associated with exposure to chemicals, understanding of relationships between dose and adverse effect, extrapolation of effects from high experimental doses to low doses associated with actual exposures, and extrapolation from effects observed in animals to effects expected...

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...