Special Issue on Robust Methods for Classification and Data Analysis

Abstract

It is now widely recognized that most statistical techniques are not resistant to outliers or other deviations from the classical assumptions. Therefore, the development of highly robust and efficient statistical methods has become a goal of paramount importance in both theoretical and applied statistics. The demand for such methods has been driven by the increasing availability of data in almost any area of scientific research. These data sets are not only becoming larger in size, but also in complexity. The extraction of essential features and the discovery of structures and relations in complex data sets must not break down when atypical observations are present. Moreover, there is a need for the development of effective diagnostics that can help to pinpoint these outliers. With many variables at hand, outlying observations can be hard to detect. Outliers need not necessarily be gross errors, but may instead contain valuable information. For instance, by means of robust methods one may discover the existence of several populations instead of one. While robust statistical methods and diagnostic tools are well established for studying data sets in simple univariate models, this is not yet the case for more complicated multivariate situations which may contain multiple groups.