The aim of detecting outliers in a multivariate sample can be pursued in different ways. We investigate here the … distribution outliers can be placed until certain simultaneous identification rules will detect them as outliers. We consider …

The aim of detecting outliers in a multivariate sample can be pursued in different ways. We investigate here the … distribution outliers can be placed until certain simultaneous identification rules will detect them as outliers. We consider …

In regression analysis, the presence of outliers in the dataset can strongly distort the classical least …-squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the … methods resist only some specific types of outliers and turn out to be ineffective under alternative scenarios. In this …

of outliers by comparing a regression estimator that is robust to outliers (S-estimator), with another that is more e …

In the robust statistics literature, a wide variety of models have been developed to cope with outliers in a rather …

In the robust statistics literature, a wide variety of models have been devel- oped to cope with outliers in a rather …

not be possible that some small amount of outliers makes a score look reliable if it is not. …

not be possible that some small amount of outliers makes a score look reliable if it is not. …

not be possible that some small amount of outliers makes a score look reliable if it is not. …

check whether outliers are present because their existence could induce significant biases. In this article, we present the … multivariate scales. These estimators can be used to robustify Mahalanobis distances and to identify outliers. Verardi and Croux … the mcd command. The implemented algorithm is relatively fast and, as we show in the simulation example section …