Robust methods for multivariate analysis — a tutorial review

Robust methods developed in statistics and chemometrics for multivariate calibration and exploratory analysis are reviewed. Robust methods can be classified according to aim: (i) regression methods, (ii) methods for outlier detection (diagnostics), and (iii) methods for dimensionality reduction (exploratory analysis). Based on this taxonomy, some of the methods are described in detail and illustrated with examples.