Data from the automatic monitoring of intensive care patients exhibits trends, outliers, and level changes as well as periods of relative constancy. All this is overlaid with a high level of noise and there are dependencies between the different items measured. Current monitoring systems tend to...

For the Michaelis-Menten model we determine designs which maximize the minimum of the D-efficiencies over a certain interval for the non-linear parameter. The best two point designs can be found explicitly and a characterization is given, when these designs are optimal within the class of all...

In this paper we describe the special role of moment theory for the construction of optimal designs in statistical regression models. A careful introduction in the problem of designing experiments for certain polynomial regression models is given, and it is demonstrated that the maximization of...

This paper analyses the relationship between organizational complexity ( the degree of detail of information necessary to correctly assign agents to positions), robustness (the relative loss of performance due to mis-allocated agents), and performance. More complex structures are not necessarily...

Additive modelling has been widely used in nonparametric regression to circumvent the "curse of dimensionality", by reducing the problem of estimating a multivariate regression function to the estimation of its univariate components. Estimation of these univariate functions, however, can suffer...

Additive modelling is known to be useful for multivariate nonparametric regression as it reduces the complexity of problem to the level of univariate regression. This usefulness could be compromised if the data set was contaminated by outliers whose detection and removal are particularly...

Nonparametric correlation estimators as the Kendall and Spearman correlation are widely used in the applied sciences. They are often said to be robust, in the sense of being resistant to outlying observations. In this paper we formally study their robustness by means of their influence functions...

Robust control theory is a tool for assessing decision rules when a decision maker distrusts either the specification of transition laws or the distribution of hidden state variables or both. Specification doubts inspire the decision maker to want a decision rule to work well for a ∅ of models...