Marginal Modelling of Categorical Data from Crossover Experiments

Conceptual Data Modelling from a Categorical Perspective

For successful information systems development, conceptual data modeling is essential. Nowadays many techniques for conceptual data modeling exist. In-depth comparisons of concepts of these techniques are very diicult as the mathematical formalizations of these techniques, if they exist at all, are very diierent. As such there is a need for a unifying formal framework providing a suuciently hig...

Bayesian Analysis of Ordered Categorical Data from Industrial Experiments

Mixture modelling of gene expression data from microarray experiments

MOTIVATION Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present nov...

Statistical Modelling for Data from Experiments with Short Hairpin RNAs

This paper delivers an example of applying intelligent data analysis to biological data where the success of the project was only possible due to joint efforts of the experts from biology, medicine and data analysis. The initial and seemingly obvious approach for the analysis of the data yielded results that did not look plausible to the biologists and medical doctors. Only a better understandi...

Clustering From Categorical Data Sequences

The three-parameter cluster model is a combinatorial stochastic process that generates categorical response sequences by randomly perturbing a fixed clustering parameter. This clear relationship between the observed data and the underlying clustering is particularly attractive in cluster analysis, in which supervised learning is a common goal and missing data is a familiar issue. The model is w...