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ROBUST@Leuven

Welcome to the webpages of ROBUST@Leuven, the research group on Robust Statistics, part of the Section of Statistics, Department of Mathematics at the University of Leuven.

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 and bio-informatics. 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. Besides the robustness of kernel methods is studied.

Our research group currently consists of the following people:

Former members include:

  • Guy Brys
  • Michiel Debruyne
  • Sanne Engelen
  • Karlien Vanden Branden
  • Stephan Van der Veeken
  • Johan Van Kerckhoven
  • 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.

In January 2009, our paper 'ROBPCA: a new approach to robust principal component analysis' (Technometrics, 2005) has been identified by Thomson Reuters’ Essential Science Indicators SM to be one of the most cited papers in the research area of “ROBUST PRINCIPAL COMPONENT ANALYSIS.” As such, it is selected as a featured Fast Moving Front paper.