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dc.contributor.authorDai, Wenlin
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2017-12-14T12:34:03Z
dc.date.available2017-12-14T12:34:03Z
dc.date.issued2018
dc.identifier.citationDai W, Genton MG (2018) An Outlyingness Matrix for Multivariate Functional Data Classification. Statistica Sinica. Available: http://dx.doi.org/10.5705/ss.202016.0537.
dc.identifier.issn1017-0405
dc.identifier.doi10.5705/ss.202016.0537
dc.identifier.urihttp://hdl.handle.net/10754/626367
dc.description.abstractThe classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation of curves that combines the direction of outlyingness with conventional statistical depth. We propose two classifiers based on directional outlyingness and the outlyingness matrix, respectively. Our classifiers provide better performance compared with existing depth-based classifiers when applied on both univariate and multivariate functional data from simulation studies. We also test our methods on two data problems: speech recognition and gesture classification, and obtain results that are consistent with the findings from the simulated data.
dc.description.sponsorshipThe authors thank the editor, the associate editor and the two referees for their constructive comments that led to a substantial improvement of the paper. The work of Wenlin Dai and Marc G. Genton was supported by King Abdullah University of Science and Technology (KAUST).
dc.publisherStatistica Sinica (Institute of Statistical Science)
dc.relation.urlhttp://www3.stat.sinica.edu.tw/ss_newpaper/SS-2016-0537_na.pdf
dc.rightsArchived with thanks to Statistica Sinica
dc.subjectDirectional outlyingness
dc.subjectFunctional data classification
dc.subjectMultivariate functional data
dc.subjectOutlyingness matrix
dc.subjectStatistical depth
dc.titleAn Outlyingness Matrix for Multivariate Functional Data Classification
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalStatistica Sinica
dc.eprint.versionPost-print
dc.identifier.arxivid1704.02568
kaust.personDai, Wenlin
kaust.personGenton, Marc G.
refterms.dateFOA2018-06-13T19:10:45Z
dc.date.published-online2018
dc.date.published-print2018


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