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Shape bias of robust covariance estimators: an empirical study

M. Hubert (), Peter Rousseeuw () and K. Vakili

Statistical Papers, 2014, vol. 55, issue 1, 15-28

Abstract: Detecting outliers in a multivariate point cloud is not trivial, especially when dealing with a sizable fraction of contamination. Over time, it has increasingly been recognized that the safest and most feasible approach to exposing outliers starts by computing a highly robust estimator of location and scatter that can withstand a large proportion of contamination. Many such estimators have been proposed in recent years. We will compare the worst-case bias of several prominent robust multivariate estimators by means of simulation. We also propose a new tool to compare robust estimators on real data sets, and illustrate it. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Outlier identification; Multivariate estimation; Robust estimation; 62H12 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s00362-013-0544-8

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