Cronbach’s alpha is a popular method to measure reliability, e.g. in quantifying the reliability of a score to summarize the information of several items in questionnaires. The alpha coefficient is known to be non-robust. We study the behavior of this coefficient in different settings to...

The repeated median line estimator is a highly robust method for fitting a regression line to a set of n data points in the plane. In this paper, we consider the problem of updating the estimate after a point is removed from or added to the data set. This problem occurs e.g. in statistical...

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...