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Statistics > Methodology

arXiv:2307.11239 (stat)
[Submitted on 20 Jul 2023]

Title:Edgewise outliers of network indexed signals

Authors:Christopher Rieser, Anne Ruiz-Gazen, Christine Thomas-Agnan
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Abstract:We consider models for network indexed multivariate data involving a dependence between variables as well as across graph nodes.
In the framework of these models, we focus on outliers detection and introduce the concept of edgewise outliers. For this purpose, we first derive the distribution of some sums of squares, in particular squared Mahalanobis distances that can be used to fix detection rules and thresholds for outlier detection. We then propose a robust version of the deterministic MCD algorithm that we call edgewise MCD. An application on simulated data shows the interest of taking the dependence structure into account. We also illustrate the utility of the proposed method with a real data set.
Subjects: Methodology (stat.ME); Machine Learning (cs.LG); Applications (stat.AP); Other Statistics (stat.OT)
MSC classes: 62
ACM classes: G.3; I.5
Cite as: arXiv:2307.11239 [stat.ME]
  (or arXiv:2307.11239v1 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.2307.11239

Submission history

From: Christopher Rieser [view email]
[v1] Thu, 20 Jul 2023 21:22:02 UTC (4,093 KB)
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