Peter J Rousseeuw


Ontology type: schema:Person     


Person Info

NAME

Peter J

SURNAME

Rousseeuw

Publications in SciGraph latest 50 shown

  • 2018-12 Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” in STATISTICAL METHODS & APPLICATIONS
  • 2018-03 Comparing Reverse Complementary Genomic Words Based on Their Distance Distributions and Frequencies in INTERDISCIPLINARY SCIENCES: COMPUTATIONAL LIFE SCIENCES
  • 2017-09 Multivariate and functional classification using depth and distance in ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
  • 2017 Dissimilar Symmetric Word Pairs in the Human Genome in 11TH INTERNATIONAL CONFERENCE ON PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS
  • 2015-09 Comments on: Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination in TEST
  • 2015-07 Multivariate functional outlier detection in STATISTICAL METHODS & APPLICATIONS
  • 2015-07 Rejoinder to ‘multivariate functional outlier detection’ in STATISTICAL METHODS & APPLICATIONS
  • 2014-02 Shape bias of robust covariance estimators: an empirical study in STATISTICAL PAPERS
  • 2013 High-Breakdown Estimators of Multivariate Location and Scatter in ROBUSTNESS AND COMPLEX DATA STRUCTURES
  • 2011 Multivariate Techniques: Robustness in INTERNATIONAL ENCYCLOPEDIA OF STATISTICAL SCIENCE
  • 2010-09 Special Issue on Robust Methods for Classification and Data Analysis in ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
  • 2010 DetMCD in a Calibration Framework in PROCEEDINGS OF COMPSTAT'2010
  • 2008-06 Efficient Algorithms for Maximum Regression Depth in DISCRETE & COMPUTATIONAL GEOMETRY
  • 2006-01 Computing LTS Regression for Large Data Sets in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2004 A Study of Belgian Inflation, Relative Prices and Nominal Rigidities using New Robust Measures of Skewness and Tail Weight in THEORY AND APPLICATIONS OF RECENT ROBUST METHODS
  • 2003-04 Efficient computation of location depth contours by methods of computational geometry in STATISTICS AND COMPUTING
  • 2003-02 Fitting multiplicative models by robust alternating regressions in STATISTICS AND COMPUTING
  • 2003 A Robust Hotelling Test in DEVELOPMENTS IN ROBUST STATISTICS
  • 2003 Robust PCA for High-dimensional Data in DEVELOPMENTS IN ROBUST STATISTICS
  • 2002-07 Location adjustment for the minimum volume ellipsoid estimator in STATISTICS AND COMPUTING
  • 2002-04 A robust Hotelling test in METRIKA
  • 2002 A Hotelling Test Based on MCD in COMPSTAT
  • 2002 A Depth Test for Symmetry in GOODNESS-OF-FIT TESTS AND MODEL VALIDITY
  • 2001 Similarities Between Location Depth and Regression Depth in STATISTICS IN GENETICS AND IN THE ENVIRONMENTAL SCIENCES
  • 2000-04 The Competitive Advantage of Seaports in INTERNATIONAL JOURNAL OF MARITIME ECONOMICS
  • 2000 A Robust Method for Multivariate Regression in DATA ANALYSIS, CLASSIFICATION, AND RELATED METHODS
  • 2000 An algorithm for deepest multiple regression in COMPSTAT
  • 2000 An improved algorithm for robust PCA in COMPSTAT
  • 2000 A robust version of principal factor analysis in COMPSTAT
  • 2000 An algorithm for the multivariate Tukey median in COMPSTAT
  • 2000 An Algorithm for Positive-Breakdown Regression Based on Concentration Steps in DATA ANALYSIS
  • 2000 A fast algorithm for highly robust regression in data mining in COMPSTAT
  • 1999-10 The depth function of a population distribution in METRIKA
  • 1999-09 Depth in an Arrangement of Hyperplanes in DISCRETE & COMPUTATIONAL GEOMETRY
  • 1998-08 Computing location depth and regression depth in higher dimensions in STATISTICS AND COMPUTING
  • 1998 The Deepest Fit in COMPSTAT
  • 1996 ISODEPTH: A Program for Depth Contours in COMPSTAT
  • 1996 Robust Regression with a Categorical Covariable in ROBUST STATISTICS, DATA ANALYSIS, AND COMPUTER INTENSIVE METHODS
  • 1994 High Breakdown Regression by Minimization of a Scale Estimator in COMPSTAT
  • 1993 Fuzzy Clustering by Minimizing the Total Hypervolume in INFORMATION AND CLASSIFICATION
  • 1992 Time-Efficient Algorithms for Two Highly Robust Estimators of Scale in COMPUTATIONAL STATISTICS
  • 1986-09 Applying robust regression techniques to institutional data in RESEARCH IN HIGHER EDUCATION
  • 1985-12 Change-of-variance sensitivities in regression analysis in PROBABILITY THEORY AND RELATED FIELDS
  • 1984-03 Numerical study of the relaxation of one-dimensional gravitational systems in ASTROPHYSICS AND SPACE SCIENCE
  • 1984 Robust Regression by Means of S-Estimators in ROBUST AND NONLINEAR TIME SERIES ANALYSIS
  • 1984 Resistant Line Fitting in Actuarial Science in PREMIUM CALCULATION IN INSURANCE
  • 1982-12 Most robust M-estimators in the infinitesimal sense in PROBABILITY THEORY AND RELATED FIELDS
  • Affiliations

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "affiliation": [
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.5596.f", 
              "type": "Organization"
            }, 
            "isCurrent": true, 
            "type": "OrganizationRole"
          }, 
          {
            "id": "https://www.grid.ac/institutes/grid.5284.b", 
            "type": "Organization"
          }
        ], 
        "familyName": "Rousseeuw", 
        "givenName": "Peter J", 
        "id": "sg:person.0775337371.63", 
        "identifier": {
          "name": "orcid_id", 
          "type": "PropertyValue", 
          "value": [
            "0000-0002-3807-5353"
          ]
        }, 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0775337371.63", 
          "https://orcid.org/0000-0002-3807-5353"
        ], 
        "sdDataset": "persons", 
        "sdDatePublished": "2019-03-07T14:31", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-researchers-20181010/20181011/dim_researchers/base/researchers_273.json", 
        "type": "Person"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/person.0775337371.63'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/person.0775337371.63'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/person.0775337371.63'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/person.0775337371.63'


     




    Preview window. Press ESC to close (or click here)


    ...