to outliers and of sparsity; that is, having relatively few nonzero factor loadings. Compared to more traditional factor …

to outliers and of sparsity; that is, having relatively few nonzero factor loadings. Compared to more traditional factor …

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 …