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Welcome |
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Welcome to the homepage of the research group on Robust Statistics, part of the Section of Statistics,
Department of Mathematics at the Katholieke Universiteit 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. Recently, 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
robust measures of skewness and tail weight have been introduced.
We study robustness of kernel methods, and regression quantiles
for censored data.
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Our research group currently consists of the following people:
Mia Hubert
Peter Rousseeuw
Michiel Debruyne (University of Antwerp)
Anna Ivanova
Stephan Van der Veeken
Dina Vanpaemel
Sabine Verboven (University of Antwerp)
Tim Verdonck (University of Antwerp).
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Former members include:
Guy Brys
Sanne Engelen
Karlien Vanden Branden.
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This website provides a complete list of our publications as well as corresponding programs. A large
number of robust methods for low- and high-dimensional data-analysis is collected in our Matlab toolbox LIBRA.
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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.
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