Nonparametric geometric outlier detection
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Publication:3299036
DOI10.1111/SJOS.12399zbMATH Open1443.62128arXiv1811.05169OpenAlexW2951763377WikidataQ127752677 ScholiaQ127752677MaRDI QIDQ3299036
Publication date: 17 July 2020
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Abstract: Outlier detection is a major topic in robust statistics due to the high practical significance of anomalous observations. Many existing methods are, however, either parametric or cease to perform well when the data is far from linearly structured. In this paper, we propose a quantity, Delaunay outlyingness, that is a nonparametric outlyingness score applicable to data with complicated structure. The approach is based a well known triangulation of the sample, which seems to reflect the sparsity of the pointset to different directions in a useful way. In addition to appealing to heuristics, we derive results on the asymptotic behaviour of Delaunay outlyingness in the case of a sufficiently simple set of observations. Simulations and an application to financial data are also discussed.
Full work available at URL: https://arxiv.org/abs/1811.05169
Statistics on manifolds (62R30) Nonparametric robustness (62G35) Statistics of extreme values; tail inference (62G32) Triangulating (57R05)
Related Items (5)
Geometric anomaly detection in data ⋮ A geometric analysis of subspace clustering with outliers ⋮ Design-based estimation for geometric quantiles with application to outlier detection ⋮ A Geometrical–Statistical Approach to Outlier Removal for TDOA Measurements ⋮ Title not available (Why is that?)
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