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

被引：0
作者：
Steffen Liebscher
Thomas Kirschstein
Claudia Becker
机构：
[1] Martin-Luther-University,
来源：
Statistics and Computing | 2013年 / 23卷
关键词：
Breakdown point; Delaunay triangulation; Minimum covariance determinant; Robust estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：677 / 688
页数：11
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