Robust methods are very useful in multivariate statistical analysis since the presence of outliers and departures from the usual model assumptions are very frequent in multivariate data.

Three robust estimation methods of the parameters of the redundancy analysis model (based on a robust correlation matrix, partial least squares and projection pursuit) are presented. Artificial data from a simulation study, designed to compare the methods, is used to see how they perform. Methods based on a robust correlation matrix and on the projection pursuit procedure show better results.

CEMAT - Center for Computational and Stochastic Mathematics