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dc.contributor.authorHuang, Huang
dc.contributor.authorSun, Ying
dc.date.accessioned2017-12-28T07:32:16Z
dc.date.available2017-12-28T07:32:16Z
dc.date.issued2016-11-15
dc.identifier.urihttp://hdl.handle.net/10754/626561
dc.description.abstractThere has been extensive work on data depth-based methods for robust multivariate data analysis. Recent developments have moved to infinite-dimensional objects such as functional data. In this work, we propose a new notion of depth, the total variation depth, for functional data. As a measure of depth, its properties are studied theoretically, and the associated outlier detection performance is investigated through simulations. Compared to magnitude outliers, shape outliers are often masked among the rest of samples and harder to identify. We show that the proposed total variation depth has many desirable features and is well suited for outlier detection. In particular, we propose to decompose the total variation depth into two components that are associated with shape and magnitude outlyingness, respectively. This decomposition allows us to develop an effective procedure for outlier detection and useful visualization tools, while naturally accounting for the correlation in functional data. Finally, the proposed methodology is demonstrated using real datasets of curves, images, and video frames.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1611.04913v1
dc.relation.urlhttp://arxiv.org/pdf/1611.04913v1
dc.rightsArchived with thanks to arXiv
dc.titleTotal Variation Depth for Functional Data
dc.typePreprint
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.identifier.arxividarXiv:1611.04913
kaust.personHuang, Huang
kaust.personSun, Ying
dc.versionv1
refterms.dateFOA2018-06-14T09:21:37Z


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