Wikipedia

Peter Rousseeuw

Peter J. Rousseeuw (born 13 October 1956) 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, which led to a book on influence functions.[1] 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. Next he was a senior researcher at Renaissance Technologies. He then returned to Belgium as professor at KU Leuven,[2][3] until becoming emeritus in 2022. His former PhD students include Annick Leroy, Hendrik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers.[4]

Peter J. Rousseeuw
Peter Rousseeuw in 2022
Born (1956-10-13) 13 October 1956 (age 68)
Wilrijk, Belgium
NationalityBelgian
EducationVrije Universiteit Brussel
ETH Zurich
Scientific career
FieldsStatistics
InstitutionsDelft University of Technology
University of Fribourg
University of Antwerp
Renaissance Technologies
KU Leuven
Thesis New Infinitesimal Methods in Robust Statistics  (1981)
Doctoral advisorFrank Hampel
Jean Haezendonck
Doctoral studentsMia Hubert

Research

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Rousseeuw has constructed and published many useful techniques.[3][5][6] He proposed the Least Trimmed Squares method[7][8][9] and S-estimators[10] for robust regression, which can resist outliers in the data.

He also introduced the Minimum Volume Ellipsoid and Minimum Covariance Determinant methods[11][12] for robust scatter matrices. This work led to his book Robust Regression and Outlier Detection with Annick Leroy.

With Leonard Kaufman he coined the term medoid when proposing the k-medoids method[13][14] for cluster analysis, also known as Partitioning Around Medoids (PAM). His silhouette display[15] shows the result of a cluster analysis, and the corresponding silhouette coefficient is often used to select the number of clusters. The work on cluster analysis led to a book titled Finding Groups in Data.[16] Rousseeuw was the original developer of the R package cluster along with Mia Hubert and Anja Struyf.[17]

The Rousseeuw–Croux scale estimator  [18] is an efficient alternative to the median absolute deviation (see robust measures of scale).

With Ida Ruts and John Tukey he introduced the bagplot,[19] 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[20] and functional data, and on robust principal component analysis.[21] His current research is on visualization of classification[22][23] and cellwise outliers.[24][25]

Recognition

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Rousseeuw was elected Member of International Statistical Institute (1991), Fellow of Institute of Mathematical Statistics (1993), and Fellow of the American Statistical Association (1994). His 1984 paper on robust regression[7] has been reprinted in Breakthroughs in Statistics,[26] which collected and annotated the 60 most influential papers in statistics from 1850 to 1990. He became an ISI highly cited researcher in 2003, and was awarded the Jack Youden Prize (2018) and the Frank Wilcoxon Prize (2021). In 2024, he received the Gottfried E. Distinguished Scholar Award of the American Statistical Association.

Creation of the Rousseeuw Prize for Statistics

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From 2016 onward Peter Rousseeuw worked on creating a new biennial prize, sponsored by him.[27] The goal of the prize is to recognize outstanding statistical innovations with impact on society, and to promote awareness of the important role and intellectual content of statistics and its profound impact on human endeavors. The award amount is 1 million US dollars, similar to the Nobel Prize in other fields. The first award in 2022 went to the topic of Causal Inference in Medicine and Public Health. It was presented by His Majesty King Philippe of Belgium to the laureates James Robins, Andrea Rotnitzky, Thomas Richardson, Miguel Hernán and Eric Tchetgen Tchetgen.

References

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  1. ^ Hampel, Frank; Ronchetti, Elvezio; Rousseeuw, Peter J.; Stahel, Werner (1986). Robust statistics: the approach based on influence functions. New York: Wiley. doi:10.1002/9781118186435. ISBN 978-0-471-73577-9.
  2. ^ "KU Leuven who's who - Peter Rousseeuw". Ku Leuven. Retrieved 21 December 2015.
  3. ^ a b "ROBUST@Leuven – Departement Wiskunde KU Leuven". Ku Leuven. Retrieved 21 December 2015.
  4. ^ "Peter Rousseeuw". The Mathematics Genealogy Project.
  5. ^ "Peter Rousseeuw". Google Scholar. Retrieved 21 December 2015.
  6. ^ "Peter Rousseeuw". ResearchGate. Retrieved 6 November 2022.
  7. ^ a b Rousseeuw, Peter J. (1984). "Least Median of Squares Regression". Journal of the American Statistical Association. 79 (388): 871–880. CiteSeerX 10.1.1.464.928. doi:10.1080/01621459.1984.10477105.
  8. ^ Rousseeuw, Peter J.; Van Driessen, Katrien (2006). "Computing LTS Regression for Large Data Sets". Data Mining and Knowledge Discovery. 12 (1): 29–45. doi:10.1007/s10618-005-0024-4. S2CID 207113006.
  9. ^ Rousseeuw, Peter J.; Leroy, Annick M. (1987). Robust Regression and Outlier Detection (3. print. ed.). New York: Wiley. doi:10.1002/0471725382. ISBN 978-0-471-85233-9.
  10. ^ Rousseeuw, P.; Yohai, V. (1984). "Robust Regression by Means of S-Estimators". Robust and Nonlinear Time Series Analysis. Lecture Notes in Statistics. Vol. 26. pp. 256–272. doi:10.1007/978-1-4615-7821-5_15. ISBN 978-0-387-96102-6.
  11. ^ Rousseeuw, Peter J.; van Zomeren, Bert C. (1990). "Unmasking Multivariate Outliers and Leverage Points". Journal of the American Statistical Association. 85 (411): 633–639. doi:10.1080/01621459.1990.10474920.
  12. ^ Rousseeuw, Peter J.; Van Driessen, Katrien (1999). "A Fast Algorithm for the Minimum Covariance Determinant Estimator". Technometrics. 41 (3): 212–223. doi:10.1080/00401706.1999.10485670.
  13. ^ Kaufman, L.; Rousseeuw, P.J. (1987). "Clustering by means of Medoids". Statistical Data Analysis Based on the L1–Norm and Related Methods, edited by Y. Dodge, North-Holland: 405–416. {{cite journal}}: Cite journal requires |journal= (help)
  14. ^ Kaufman, Leonard; Rousseeuw, Peter J. (1990). Finding groups in data: an introduction to cluster analysis. New York: Wiley. doi:10.1002/9780470316801. ISBN 978-0-471-87876-6.
  15. ^ Rousseeuw, Peter J. (1987). "Silhouettes: A graphical aid to the interpretation and validation of cluster analysis". Journal of Computational and Applied Mathematics. 20: 53–65. doi:10.1016/0377-0427(87)90125-7.
  16. ^ Kaufman, Leonard; Rousseeuw, Peter J. (1990). Finding groups in data: an introduction to cluster analysis. New York: Wiley. doi:10.1002/9780470316801. ISBN 978-0-471-87876-6.
  17. ^ cluster: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al., 2021-04-17, retrieved 2021-05-27
  18. ^ Rousseeuw, Peter J.; Croux, Christophe (1993). "Alternatives to the Median Absolute Deviation". Journal of the American Statistical Association. 88 (424): 1273. doi:10.2307/2291267. JSTOR 2291267.
  19. ^ Rousseeuw, Peter J.; Ruts, Ida; Tukey, John W. (1999). "The bagplot: a bivariate boxplot". The American Statistician. 53 (4): 382–387. doi:10.1080/00031305.1999.10474494.
  20. ^ Rousseeuw, Peter J.; Hubert, Mia (1999). "Regression Depth". Journal of the American Statistical Association. 94 (446): 388. doi:10.2307/2670155. JSTOR 2670155.
  21. ^ Hubert, Mia; Rousseeuw, Peter J; Vanden Branden, Karlien (2005). "ROBPCA: A New Approach to Robust Principal Component Analysis". Technometrics. 47 (1): 64–79. doi:10.1198/004017004000000563. S2CID 5071469.
  22. ^ Raymaekers, Jakob; Rousseeuw, Peter J.; Hubert, Mia (2022). "Class Maps for Visualizing Classification Results". Technometrics. 64 (2): 151–165. arXiv:2007.14495. doi:10.1080/00401706.2021.1927849. eISSN 1537-2723. ISSN 0040-1706.
  23. ^ Raymaekers, Jakob; Rousseeuw, Peter J. (4 April 2022). "Silhouettes and Quasi Residual Plots for Neural Nets and Tree-based Classifiers". Journal of Computational and Graphical Statistics. 31 (4): 1332–1343. arXiv:2106.08814. doi:10.1080/10618600.2022.2050249. eISSN 1537-2715. ISSN 1061-8600.
  24. ^ Rousseeuw, Peter J.; Van Den Bossche, Wannes (2018). "Detecting Deviating Data Cells". Technometrics. 60 (2): 135–145. arXiv:1601.07251. doi:10.1080/00401706.2017.1340909. eISSN 1537-2723. ISSN 0040-1706.
  25. ^ Raymaekers, Jakob; Rousseeuw, Peter J. (2021). "Fast Robust Correlation for High-Dimensional Data". Technometrics. 63 (2): 184–198. arXiv:1712.05151. doi:10.1080/00401706.2019.1677270. eISSN 1537-2723. ISSN 0040-1706.
  26. ^ Kotz, Samuel; Johnson, Norman (1992). Breakthroughs in Statistics. Vol. III. New York: Springer. doi:10.1007/978-1-4612-0667-5. ISBN 978-0-387-94988-8.
  27. ^ "The Rousseeuw Prize for Statistics". Rousseeuw Prize. Retrieved 1 November 2022.
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