Peter Rousseeuw

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Peter J. Rousseeuw
Peter Rousseeuw in 2022.png
Peter Rousseeuw in 2022
Born (1956-10-13) 13 October 1956 (age 67)
Wilrijk, Belgium
Nationality Belgian
Education Vrije Universiteit Brussel
ETH Zurich
Scientific career
Fields Statistics
Institutions Delft University of Technology
University of Fribourg
University of Antwerp
Renaissance Technologies
KU Leuven
Thesis New Infinitesimal Methods in Robust Statistics  (1981)
Doctoral advisor Frank Hampel
Jean Haezendonck
Doctoral students Mia Hubert

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]

Contents

Research

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

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

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.

Related Research Articles

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<span class="mw-page-title-main">Influential observation</span> Observation that would cause a large change if deleted

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In robust statistics, repeated median regression, also known as the repeated median estimator, is a robust linear regression algorithm. The estimator has a breakdown point of 50%. Although it is equivariant under scaling, or under linear transformations of either its explanatory variable or its response variable, it is not under affine transformations that combine both variables. It can be calculated in time by brute force, in time using more sophisticated techniques, or in randomized expected time. It may also be calculated using an on-line algorithm with update time.

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Mia Hubert is a Belgian mathematical statistician known for her research on topics in robust statistics including medoid-based clustering,[a] regression depth,[b] the medcouple for robustly measuring skewness,[c] box plots for skewed data,[f] and robust principal component analysis,[d] and for her implementations of robust statistical algorithms in the R statistical software system, MATLAB,[e] and S-PLUS.[a] She is a professor in the statistics and data science section of the department of mathematics at KU Leuven.

Robust Regression and Outlier Detection is a book on robust statistics, particularly focusing on the breakdown point of methods for robust regression. It was written by Peter Rousseeuw and Annick M. Leroy, and published in 1987 by Wiley.

References

  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. 1 2 "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. 1 2 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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