Multivariate Functional Data Visualization and Outlier Detection

Handle URI:
http://hdl.handle.net/10754/626524
Title:
Multivariate Functional Data Visualization and Outlier Detection
Authors:
Dai, Wenlin; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
arXiv
Issue Date:
19-Mar-2017
ARXIV:
arXiv:1703.06419
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1703.06419v2; http://arxiv.org/pdf/1703.06419v2
Appears in Collections:
Other/General Submission; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDai, Wenlinen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-12-28T07:32:14Z-
dc.date.available2017-12-28T07:32:14Z-
dc.date.issued2017-03-19en
dc.identifier.urihttp://hdl.handle.net/10754/626524-
dc.description.abstractThis article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1703.06419v2en
dc.relation.urlhttp://arxiv.org/pdf/1703.06419v2en
dc.rightsArchived with thanks to arXiven
dc.titleMultivariate Functional Data Visualization and Outlier Detectionen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.identifier.arxividarXiv:1703.06419en
kaust.authorDai, Wenlinen
kaust.authorGenton, Marc G.en
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