ABSTRACT
The word robustness is frequently used in the literature, and is often stated with completely different meaning. In this contribution robustness means to reduce the influence of “unusual” observations on statistical estimates. Such observations are frequently denoted as outliers, and are often thought to be extreme values caused by measurement or transcription errors. However, the notion of outliers also includes observations (or groups of observations) which are inconsistent with the remaining data set. The judgment whether obser vations are declared as outliers or as “inliers” is sometimes subjective, and robust statistics should serve as a tool for an objective decision. In terms of a statistical model, robust statistics could be defined as follows: “In a broad in formal sense, robust statistics is a body of knowledge, partly formalized into theory of robustness ’, relating to deviations from idealized assumptions in s ta tis t ics (Hampel, Ronchetti, Rousseeuw, & Stahel, 1986). Hence, robust statistics is aimed at yielding reliable results in cases where classical assump tions like normality, independence or linearity are violated.
