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.
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Hubert, M., Rousseeuw, P. & Vakili, K. Shape bias of robust covariance estimators: an empirical study. Stat Papers 55, 15–28 (2014). https://doi.org/10.1007/s00362-013-0544-8
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DOI: https://doi.org/10.1007/s00362-013-0544-8