# Peter Rousseeuw

Peter J. Rousseeuw
Picture of Peter Rouseeuw
Born13 October 1956 (age 65)
Wilrijk, Belgium
NationalityBelgian
Alma materVrije Universiteit Brussel
Scientific career
FieldsStatistics
InstitutionsDelft University of Technology
University of Fribourg
University of Antwerp
Renaissance Technologies
KU Leuven
Doctoral studentsMia 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 in the group of Frank Hampel, 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. Currently he is professor at KU Leuven, Belgium.[2][3] 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.[4]

## Research

Rousseeuw has authored many publications.[3][5] He proposed the Least Trimmed Squares method [6] [7] [8] and S-estimators [9] for robust regression, which can resist outliers in the data. He also introduced the Minimum Volume Ellipsoid and Minimum Covariance Determinant methods [10] [11] for robust scatter matrices. With L. Kaufman he coined the term medoid when proposing the k-medoids method [12][13] for cluster analysis, also known as Partitioning Around Medoids (PAM). His silhouette display [14] 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 ${\displaystyle Q_{n}}$ [15] is an efficient alternative to the median absolute deviation (see robust measures of scale). 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[16] and functional data, and on robust principal component analysis .[17] His 1984 paper has been reprinted in Breakthroughs in Statistics, which collected and annotated the 60 most influential papers in statistics from 1850 to 1990.[6][18]

He was the original developer of the R package cluster along with Mia Hubert and Anja Struyf.[19]

## References

1. ^ Hampel, Frank; Ronchetti, Elvezio; Rousseeuw, Peter J.; Stahel, Werner (1986). Robust statistics: the approach based on influence functions (2nd ed.). New York: Wiley. ISBN 978-0-471-73577-9.
2. ^ "KU Leuven who's who - Peter Rousseeuw". www.kuleuven.be. Retrieved 21 December 2015.
3. ^ a b "ROBUST@Leuven – Departement Wiskunde KU Leuven". wis.kuleuven.be. Retrieved 21 December 2015.
4. ^ "The Mathematics Genealogy Project - Peter Rousseeuw". www.genealogy.ams.org.
5. ^ "Peter Rousseeuw - Google Scholar Citations". scholar.google.com. Retrieved 21 December 2015.
6. ^ 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.
7. ^ 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.
8. ^ Rousseeuw, Peter J.; Leroy, Annick M. (1987). Robust Regression and Outlier Detection (3. print. ed.). New York: Wiley. ISBN 978-0-471-85233-9.
9. ^ 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.
10. ^ 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.
11. ^ 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.
12. ^ 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)
13. ^ Kaufman, Leonard; Rousseeuw, Peter J. (1990). Finding groups in data : an introduction to cluster analysis (3. print. ed.). New York: Wiley. ISBN 978-0-471-87876-6.
14. ^ 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.
15. ^ 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.
16. ^ Rousseeuw, Peter J.; Hubert, Mia (1999). "Regression Depth". Journal of the American Statistical Association. 94 (446): 388. doi:10.2307/2670155. JSTOR 2670155.
17. ^ 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.
18. ^ Kotz, Samuel; Johnson, Norman (1992). Breakthroughs in Statistics, Volume III. New York, NY: Springer New York. ISBN 978-0-387-94988-8.
19. ^ cluster: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al., 2021-04-17, retrieved 2021-05-27