The first example involves the real data given in Table 1 which are the results of an interlaboratory test. The boxplots are shown in Fig. 1 where the dotted line denotes the mean of the observations and the solid line the median. We note that only the results of the Laboratories 1 and 3 lie...

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...

The aim of detecting outliers in a multivariate sample can be pursued in different ways. We investigate here the performance of several simultaneous multivariate outlier identification rules based on robust estimators of location and scale. It has been shown that the use of estimators with high...

In their paper, Davies and Gather (1993) formalized the task of outlier identification, considering also certain performance criteria for outlier identifiers. One of those Criteria, the maximum asymptotic bias, is carried over here to multivariate outlier identifiers. We show how this term...

In investigations on the behaviour of robust estimators, typically their consistency and their asymptotic normality are studied as a necessity. Their rates of convergence, however, are often given less weight. We show here that the rate of convergence of a multivariate robust estimator to its...

In this paper, we consider one-step outlier identification rules for multivariate data-generalizing the concept of so-called a - outlier identifiers_ as presented in Davies and Gather (1993) for the case of univariate samples. We investigate how the finite sample breakdown points of estimators...

We consider a new estimator of scale for exponential samples which is most B-robust in the sense of Hampel et al. (1986). This estimator is compared with two other estimators which were proposed by Rousseeuw and Croux (1993) but for a Gaussian model. All three estimators have the same breakdown...

Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Several properties of this relatively new method have been examined already, but little attention has been paid to robustness aspects. We show that SIR is very sensitive towards outliers in the data....

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.