Special issue on robust multivariate analysis and classification

Abstract

It is now widely recognized that most classical statistical techniques are not resistant in the presence of outliers. Correspondingly, the development of highly robust and efficient statistical methods has become a goal of paramount importance in both theoretical and applied research. 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. In addition, 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 associated with “gross” contamination errors, but may instead contain valuable information. An example is the indication of the existence of several populations instead of one. While robust statistical methods and diagnostic tools are well established for studying data sets under simple univariate models, this is not the case for more complicated multivariate situations.