Statistics and Computing

, Volume 23, Issue 6, pp 677–688 | Cite as

RDELA—a Delaunay-triangulation-based, location and covariance estimator with high breakdown point

  • Steffen Liebscher
  • Thomas Kirschstein
  • Claudia Becker


We propose an approach that utilizes the Delaunay triangulation to identify a robust/outlier-free subsample. Given that the data structure of the non-outlying points is convex (e.g. of elliptical shape), this subsample can then be used to give a robust estimation of location and scatter (by applying the classical mean and covariance). The estimators derived from our approach are shown to have a high breakdown point. In addition, we provide a diagnostic plot to expand the initial subset in a data-driven way, further increasing the estimators’ efficiency.


Breakdown point Delaunay triangulation Minimum covariance determinant Robust estimation 


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Steffen Liebscher
    • 1
  • Thomas Kirschstein
    • 1
  • Claudia Becker
    • 1
  1. 1.Martin-Luther-UniversityHalle (Saale)Germany

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