Show simple item record

dc.contributor.authorDai, Wenlin
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2018-04-25T12:50:38Z
dc.date.available2017-12-28T07:32:12Z
dc.date.available2018-04-25T12:50:38Z
dc.date.issued2018-04-07
dc.identifier.citationDai W, Genton MG (2018) Directional outlyingness for multivariate functional data. Computational Statistics & Data Analysis. Available: http://dx.doi.org/10.1016/j.csda.2018.03.017.
dc.identifier.issn0167-9473
dc.identifier.doi10.1016/j.csda.2018.03.017
dc.identifier.urihttp://hdl.handle.net/10754/626485
dc.description.abstractThe direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional directional outlyingness are investigated and 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. This decomposition serves as a visualization tool for the centrality of curves. Furthermore, an outlier detection procedure is proposed 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.
dc.description.sponsorshipThis research was supported by King Abdullah University of Science and Technology (KAUST) . The authors thank the editor, an associate editor, and three anonymous referees for their valuable comments.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://arxiv.org/abs/1612.04615v4
dc.relation.urlhttp://arxiv.org/pdf/1612.04615v4
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S016794731830077X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [, , (2018-04-07)] DOI: 10.1016/j.csda.2018.03.017 . © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCentrality visualization
dc.subjectDirectional outlyingness
dc.subjectMultivariate function data
dc.subjectOutlier detection
dc.subjectOutlyingness decomposition
dc.titleDirectional outlyingness for multivariate functional data
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalComputational Statistics & Data Analysis
dc.eprint.versionPost-print
dc.identifier.arxivid1612.04615
kaust.personDai, Wenlin
kaust.personGenton, Marc G.
dc.date.accepted2019
refterms.dateFOA2020-04-07T00:00:00Z
dc.date.published-online2018-04-07
dc.date.published-print2018-04
dc.date.posted2016-12-14


Files in this item

Thumbnail
Name:
1-s2.0-S016794731830077X-main.pdf
Size:
2.198Mb
Format:
PDF
Description:
Accepted Manuscript
Thumbnail
Name:
mmc1.zip
Size:
9.057Mb
Format:
Unknown
Description:
Supplemental files
Thumbnail
Name:
mmc2.pdf
Size:
184.7Kb
Format:
PDF
Description:
Supplemental files

This item appears in the following Collection(s)

Show simple item record

VersionItemEditorDateSummary

*Selected version