When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local...

Generell hat sich im Forschungsprojekt insbesondere durch die Gespräche mit den Hochschulvertretern bestätigt, dass für qualitativ hochwertige Lehre und Forschung qualitativ hochwertige Flächen in ausreichendem Umfang notwendig sind. Ein Ziel der Forschungsarbeit ist die Entwicklung von...

Two main issues regarding data quality are data contamination (outliers) and data completion (missing data). These two problems have attracted much attention and research but surprisingly, they are seldom considered together. Popular robust methods such as <italic>S</italic>-estimators of multivariate location...

Most asymptotic results for robust estimates rely on regularity conditions that are difficult to verify and that real data sets rarely satisfy. Moreover, these results apply to fixed distribution functions. In the robustness context the distribution of the data remains largely unspecified and...

This paper extends Huber's (1964) min-max result to the case when the scale parameter is unknown and must be estimated along with the location parameter. A min-max problem in which nature chooses F from a family of symmetric distribution functions around a given location-scale central model, the...

We analyze the global robustness of location and dispersion estimates using the concept of relative explosion rate. The merits of several dispersion estimates are compared when the dispersion parameter itself is of main interest and also when they are auxiliary estimates needed to define scale...