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Peter Rousseeuw

April 08, 2024

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 67)
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 edit

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 edit

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).

Creation of the Rousseeuw Prize for Statistics edit

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 edit

  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.

April 08, 2024
peter, rousseeuw, peter, rousseeuw, born, october, 1956, statistician, known, work, robust, statistics, cluster, analysis, obtained, 1981, vrije, universiteit, brussel, following, research, carried, zurich, which, book, influence, functions, later, professor, . 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 Lopuhaa Geert Molenberghs Christophe Croux Mia Hubert Stefan Van Aelst Tim Verdonck and Jakob Raymaekers 4 Peter J RousseeuwPeter Rousseeuw in 2022Born 1956 10 13 13 October 1956 age 67 Wilrijk BelgiumNationalityBelgianEducationVrije Universiteit BrusselETH ZurichScientific careerFieldsStatisticsInstitutionsDelft University of TechnologyUniversity of FribourgUniversity of AntwerpRenaissance TechnologiesKU LeuvenThesisNew Infinitesimal Methods in Robust Statistics 1981 Doctoral advisorFrank HampelJean HaezendonckDoctoral studentsMia Hubert Contents 1 Research 2 Recognition 3 Creation of the Rousseeuw Prize for Statistics 4 ReferencesResearch editRousseeuw 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 Qn displaystyle Q n nbsp 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 editRousseeuw 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 Creation of the Rousseeuw Prize for Statistics editFrom 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 Hernan and Eric Tchetgen Tchetgen References edit 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 KU Leuven who s who Peter Rousseeuw Ku Leuven Retrieved 21 December 2015 a b ROBUST Leuven Departement Wiskunde KU Leuven Ku Leuven Retrieved 21 December 2015 Peter Rousseeuw The Mathematics Genealogy Project Peter Rousseeuw Google Scholar Retrieved 21 December 2015 Peter Rousseeuw ResearchGate Retrieved 6 November 2022 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 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 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 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 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 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 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 a href Template Cite journal html title Template Cite journal cite journal a Cite journal requires journal help 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 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 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 cluster Finding Groups in Data Cluster Analysis Extended Rousseeuw et al 2021 04 17 retrieved 2021 05 27 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 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 Rousseeuw Peter J Hubert Mia 1999 Regression Depth Journal of the American Statistical Association 94 446 388 doi 10 2307 2670155 JSTOR 2670155 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 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 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 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 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 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 The Rousseeuw Prize for Statistics Rousseeuw Prize Retrieved 1 November 2022 Retrieved from https en wikipedia org w index php title Peter Rousseeuw amp oldid 1193887941, wikipedia, wiki, book, books, library,

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