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...

We introduce and compare several robust procedures for bandwidth selection when estimating the variance function. These bandwidth selectors are to be used in combination with the robust scale estimates introduced by Boente et al. (2010a). We consider two different robust cross-validation...

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...

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...

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...