RDELA—a Delaunay-triangulation-based, location and covariance estimator with high breakdown point |
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Authors: | Steffen Liebscher Thomas Kirschstein Claudia Becker |
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Institution: | 1. Martin-Luther-University, Gr. Steinstrasse 73, 06099, Halle (Saale), Germany
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Abstract: | 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. |
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