The goal of robust statistics is to develop data analytical methods which are resistant to outlying observations in the data, and hence which are also able to detect these outliers. Pioneering work in this area has been done by Huber (1981), Hampel et al. (1986) and Rousseeuw and Leroy (1987). In their work, estimators for location, scale, scatter and regression play a central role.
Nowadays, robust estimators are being developed for many statistical models. Our research group is very active in investigating estimators of covariance and regression for high-dimensional data, with applications in chemometrics, bio-informatics and actuarial sciences. Robust estimators have been developed for PCA (principal component analysis), PCR (principal component regression), PLS (partial least squares), classification, ICA (independent component analysis) and multi-way analysis. Also several robust methods for skewed distributions are introduced, and the robustness of kernel methods is studied. Recent research interests are robust inference, statistical depth, functional data and cellwise outliers.
Our research group currently consists of the following people:
Former members include:
Guy Brys, Michiel Debruyne, Sanne Engelen, Eric Schmitt, Kaveh Vakili, Karlien Vanden Branden, Stephan Van der Veeken, Johan Van Kerckhoven, Dina Vanpaemel and Sabine Verboven.
This website provides a complete list of our publications as well as corresponding software. A large number of robust methods for low- and high-dimensional data-analysis is collected in our Matlab toolbox LIBRA.