to outliers. To address this issue, we propose a robust alternative that estimates the SUR parameters in a more outlier …

.” This article revisits an admittedly selected set of these “established facts” including how to deal with outliers …

Average treatment effects estimands can present significant bias under the presence of outliers. Moreover, outliers can … outliers. Bad and good leverage points outliers are considered. The bias arises because bad leverage points completely change … the propensity score. We provide some clues to diagnose the presence of outliers and propose a reweighting estimator that …

access to European markets, and the presence of many outliers due to different reasons. These factors must be considered in …

. However, these sample statistics are notoriously susceptible to contamination by outliers, a problem compounded by the fact … that the outliers may be invisible to conventional diagnostics. High-breakdown estimation is a procedure designed to remove … this cause for concern by producing estimates that are immune to serious distortion by a minority of outliers, regardless …

outliers and influential observations. A variety of diagnostic methods are employed to identify these influential observations … methods utilised proved incapable of identifying or accommodating the gross outlier(s) in the data, the more successful …

. We propose to use Cook's Distance in order to identify influential outliers having substantial influence on first …

, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and …