In many fields of applied statistics samples from several locations in an investigation area are taken repeatedly over time. Especially in environmental monitoring the chemical and physical conditions in water, air and soil are measured using fixed and possibly mobile monitoring stations. The...

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...

We consider hierarchical Bayes analyses of an experiment conducted to enable calibration of a set of mass-produced resistance temperature devices (RTDs). These were placed in batches into a liquid bath with a precise NIST-approved thermometer, and resistances and temperatures were recorded...

Market microstructure noise is a challenge to high-frequency based estimation of the integrated variance, because the noise accumulates with the sampling frequency. In this paper, we analyze the impact of microstructure noise on the realized range-based variance and propose a bias-correction to...

In this paper we study the connection between matrix measures and random walks with a tridiagonal block transition matrix. We derive sufficient conditions such that the blocks of the n-step transition matrix of the Markov chain can be represented as integrals with respect to a matrix valued...

This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical...