The boxplot is probably the most commonly used tool to represent the distribution of the data and identify atypical observations in a univariate dataset. The problem with the standard boxplot is that as soon as asymmetry or tail heaviness appears, the percentage of values identified as atypical...

In regression and multivariate analysis, the presence of outliers in the dataset can strongly distort classical estimations and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are...

When some observations are outlying (in one or several dimensions) PCA is distorted an may lead to incorrect results. We therefore propose a simple solution to deal with this problem by providing a short ado file. To illustrate the importance of outliers in PCA I would like to present a simple...

In the robust statistics literature, a wide variety of models has been developed to cope with outliers in a rather large number of scenarios. Nevertheless, a recurrent problem for the empirical implementation of these estimators is that optimization algorithms generally do not perform well when...

Semiparametric regression deals with the introduction of some very general nonlinear functional forms in regression analyses. This class of regression models is generally used to fit a parametric model in which the functional form of a subset of the explanatory variables is not known and/or in...