short- and long-range dependence. An iterative data-driven algorithm combines MLE and kernel estimation. Predictions combine …

distributional assumptions, the choice of the welfare statistics of interest, the procedure for computing them, outliers, undesirable …

uncertainty, we use Least Trimmed Squares, a robust estimation technique, to identify outliers and investigate how they affect the … the sample than previously claimed, and that outliers affect the results. Nevertheless trust seems quite important …

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 …

the sample. These extreme observations, or outliers, often have a large impact on the results of statistical analyses … this, the detection of outliers and their appropriate treatment is often dealt with in a rather sloppy manner. One reason … the presence of outliers. Our paper intents to improve on this situation by presenting a highly robust method for …

systematize the investigation of outlier influence on the results by using the robust estimation technique Least Trimmed Squares …. We find that when outliers (especially China) are removed, the trust-growth relationship is no longer robust. On average …