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Peter J. Rousseeuw

Peter J. Rousseeuw

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Mathematics
Belgium
2026

D-Index & Metrics

Mathematics

D-Index
81
Citations
102469
World Ranking
130
National Ranking
3

Research.com Recognitions

  • 2026 - Research.com Mathematics in Belgium Leader Award
  • 2025 - Research.com Mathematics in Belgium Leader Award
  • 2023 - Research.com Mathematics in Belgium Leader Award
  • 1994 - Fellow of the American Statistical Association (ASA)

Overview

Peter J. Rousseeuw is affiliated with KU Leuven in Belgium. Their research spans multiple disciplines, predominantly focusing on areas within mathematics and computer science. In particular, they have contributed extensively to the fields of statistics and probability as well as artificial intelligence.

The scientist has published research on a variety of topics that include:

  • Advanced Statistical Methods and Models
  • Advanced Statistical Process Monitoring
  • Statistical Methods and Inference
  • Anomaly Detection Techniques and Applications
  • Machine Learning and Data Classification
  • Spectroscopy and Chemometric Analyses
  • Bayesian Modeling and Causal Inference

Among their recent papers are the following:

  • "The Cellwise Minimum Covariance Determinant Estimator," 2023, Journal of the American Statistical Association
  • "Challenges of cellwise outliers," 2024, Econometrics and Statistics
  • "Handling Cellwise Outliers by Sparse Regression and Robust Covariance," 2021, Journal of Data Science Statistics and Visualisation
  • "Real-time outlier detection for large datasets by RT-DetMCD," 2020, Chemometrics and Intelligent Laboratory Systems
  • "Real-time discriminant analysis in the presence of label and measurement noise," 2020, Chemometrics and Intelligent Laboratory Systems

The scientist has frequently published in venues such as:

  • arXiv (Cornell University)
  • Econometrics and Statistics
  • Journal of Data Science Statistics and Visualisation
  • Journal of Computational and Graphical Statistics
  • Chemometrics and Intelligent Laboratory Systems

Key collaborators in their work include:

  • Jakob Raymaekers
  • Mia Hubert
  • Tim Verdonck
  • Fabio Centofanti
  • Sarah Leyder

Peter J. Rousseeuw's body of research centers on statistical methods that address robustness in data analysis, with a notable emphasis on outlier detection and covariance estimation techniques. Their work involves applications in statistical process monitoring and machine learning, integrating advanced methods for handling complex data and uncertainty.

The scientist has been recognized as a Fellow of the American Statistical Association since 1994.

Best Publications

  • Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

    Peter Rousseeuw

  • Robust Regression and Outlier Detection

    P. J. Rousseeuw;A. M. Leroy

  • Finding Groups in Data

    Leonard Kaufman;Peter J. Rousseeuw

  • Robust statistics: the approach based on influence functions

    Frank R. Hampel;Elvezio M. Ronchetti;Peter J. Rousseeuw;Werner A. Stahel

  • Least Median of Squares Regression

    Peter J. Rousseeuw

  • Wiley Series in Probability and Mathematical Statistics

    Peter J. Rousseeuw;Annick M. Leroy

  • Robust Regression and Outlier Detection: Rousseeuw/Robust Regression & Outlier Detection

    Peter J. Rousseeuw;Annick M. Leroy

  • A fast algorithm for the minimum covariance determinant estimator

    Peter J. Rousseeuw;Katrien Van Driessen

  • Alternatives to the Median Absolute Deviation

    Peter J. Rousseeuw;Christophe Croux

  • Clustering by means of medoids

    L Kaufman;Peter Rousseeuw

  • Unmasking Multivariate Outliers and Leverage Points

    Peter J. Rousseeuw;Bert C. van Zomeren

  • Multivariate estimation with high breakdown point

    Peter Rousseeuw

  • ROBUST REGRESSION BY MEANS OF S-ESTIMATORS

    Peter Rousseeuw;Victor Yohai

  • ROBPCA: A New Approach to Robust Principal Component Analysis

    Mia Hubert;Peter J Rousseeuw;Karlien Vanden Branden

  • Partitioning Around Medoids (Program PAM)

    Leonard Kaufman;Peter J. Rousseeuw

  • Robust statistics for outlier detection

    Peter J. Rousseeuw;Mia Hubert

  • Computing LTS Regression for Large Data Sets

    Peter J. Rousseeuw;Katrien Driessen

  • The Bagplot: A Bivariate Boxplot

    Peter J. Rousseeuw;Ida Ruts;John W. Tukey

  • Breakdown Points of Affine Equivariant Estimators of Multivariate Location and Covariance Matrices

    Hendrik P. Lopuhaa;Peter J. Rousseeuw

  • Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms.

    Erich Schubert;Peter J. Rousseeuw

  • Unmasking Multivariate Outliers and Leverage Points: Rejoinder

    Peter J. Rousseeuw;Bert C. van Zomeren

Frequent Co-Authors

Mia Hubert
Mia Hubert KU Leuven
Christophe Croux
Christophe Croux EDHEC Business School
Elvezio Ronchetti
Elvezio Ronchetti University of Geneva
Lamine Mili
Lamine Mili Virginia Tech
Joseph S. B. Mitchell
Joseph S. B. Mitchell Stony Brook University
Micha Sharir
Micha Sharir Tel Aviv University
Jack Snoeyink
Jack Snoeyink University of North Carolina at Chapel Hill
Marc van Kreveld
Marc van Kreveld Utrecht University
Bettina Speckmann
Bettina Speckmann Eindhoven University of Technology

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