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dc.contributor.authorDai, Wenlinen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-12-28T07:32:12Z
dc.date.available2017-12-28T07:32:12Z
dc.date.issued2016-12-14en
dc.identifier.urihttp://hdl.handle.net/10754/626485.1
dc.description.abstractThe direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, we generalize classical depth to directional outlyingness for functional data. We investigate theoretical properties of functional directional outlyingness and find that the total outlyingness can be naturally decomposed into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. Using this decomposition, we provide a visualization tool for the centrality of curves. Furthermore, we design an outlier detection procedure based on functional directional outlyingness. This criterion applies to both univariate and multivariate curves and simulation studies show that it outperforms competing methods. Weather and electrocardiogram data demonstrate the practical application of our proposed framework.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1612.04615v4en
dc.relation.urlhttp://arxiv.org/pdf/1612.04615v4en
dc.rightsArchived with thanks to arXiven
dc.titleDirectional outlyingness for multivariate functional dataen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.identifier.arxividarXiv:1612.04615en
kaust.personDai, Wenlin
kaust.personGenton, Marc G.


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