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dc.contributor.authorDai, Wenlin
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2018-09-27T07:48:01Z
dc.date.available2017-12-28T07:32:14Z
dc.date.available2018-09-27T07:48:01Z
dc.date.issued2018-08-24
dc.identifier.citationDai W, Genton MG (2018) Multivariate Functional Data Visualization and Outlier Detection. Journal of Computational and Graphical Statistics: 1–12. Available: http://dx.doi.org/10.1080/10618600.2018.1473781.
dc.identifier.issn1061-8600
dc.identifier.issn1537-2715
dc.identifier.doi10.1080/10618600.2018.1473781
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 nonoutlying 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. Supplementary material for this article is available online.
dc.description.sponsorshipThis research was supported by the King Abdullah University of Science and Technology (KAUST).
dc.publisherInforma UK Limited
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/10618600.2018.1473781
dc.rightsArchived with thanks to Journal of Computational and Graphical Statistics
dc.subjectData visualization
dc.subjectDirectional outlyingness
dc.subjectFunctional data
dc.subjectGraphical tool
dc.subjectMagnitude and shape
dc.subjectOutlier detection
dc.titleMultivariate Functional Data Visualization and Outlier Detection
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalJournal of Computational and Graphical Statistics
dc.eprint.versionPost-print
dc.identifier.arxivid1703.06419
kaust.personDai, Wenlin
kaust.personGenton, Marc G.
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.6308771.v1
refterms.dateFOA2018-06-13T12:34:38Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Dataset]</i> <br/> Wenlin Dai, &amp; Genton, M. G. (2018). <i>Multivariate Functional Data Visualization and Outlier Detection</i> [Data set]. Taylor &amp; Francis. https://doi.org/10.6084/M9.FIGSHARE.6308771.V1. DOI: <a href="https://doi.org/10.6084/m9.figshare.6308771.v1" >10.6084/m9.figshare.6308771.v1</a> Handle: <a href="http://hdl.handle.net/10754/664192" >10754/664192</a></a></li></ul>
dc.date.published-online2018-08-24
dc.date.published-print2018-10-02
dc.date.posted2017-03-19


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