Abstract
A class of robust methods for multivariate data analysis was proposed to enable processing of data that do not satisfy assumptions necessary for the application of classical data analysis methods. Canonical covariance analysis (Momirovic, Dobric and Karaman, 1983) was developed as the general method to analyse relationships between two sets of variables. A model of robust redundancy analysis (Prot, Bosnar and Momirovic, 1983), as well as models of robust regression (Stalec and Momirovic, 1983) and discriminant (Dobric and Momirovic, 1984) analysis, were derived as special cases from the canonical covariance model. In order to clarify the interpretation of the obtained results, relationships between robust methods and corresponding classical methods were described in our previous papers. A set of programs for all proposed methods was written in GENSTAT and SS languages, and the behaviour of the methods was examined on real data.
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