About the Speaker
Peter J. Rousseeuw (Belgium) is a statistician known for his work on robust statistics and cluster analysis. He obtained his PhD in 1981 at the Vrije Universiteit Brussel, following research carried out at the ETH in Zurich in the group of Frank Hampel, which led to a book on influence functions. Later he was professor at the Delft University of Technology, The Netherlands, at the University of Fribourg, Switzerland, and at the University of Antwerp, Belgium. Currently he is professor at KU Leuven, Belgium. He is a fellow of the Institute of Mathematical Statistics (1993) and the American Statistical Association (1994). His former PhD students include A. Leroy, H. Lopuhäa, G. Molenberghs, C. Croux, M. Hubert, S. Van Aelst and T. Verdonck. Rousseeuw has authored many publications. He proposed the Least Trimmed Squares method and S-estimators for robust regression, which can resist outliers in the data. He also introduced the Minimum Volume Ellipsoid and Minimum Covariance Determinant methods for robust scatter matrices. With L. Kaufman he coined the word medoid when proposing the k-medoids method for cluster analysis, also known as Partitioning Around Medoids (PAM). His silhouette display shows the result of a cluster analysis, and the resulting index is often used to select the number of clusters. The Rousseeuw-Croux scale estimator Q_n is an efficient alternative to the median absolute deviation. With I. Ruts and John Tukey he introduced the bagplot, a bivariate generalization of the boxplot. His more recent work has focused on concepts and algorithms for statistical depth functions in the settings of multivariate, regression and functional data, and on robust principal component analysis . His 1984 paper has been reprinted in Breakthroughs in Statistics collected and annotated the 60 most influential papers in statistics from 1850 to 1990.