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dc.contributor.authorLópez-Pintado, Sara
dc.contributor.authorSun, Ying
dc.contributor.authorLin, Juan K.
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2015-08-03T11:51:22Z
dc.date.available2015-08-03T11:51:22Z
dc.date.issued2014-03-05
dc.identifier.citationLópez-Pintado, S., Sun, Y., Lin, J. K., & Genton, M. G. (2014). Simplicial band depth for multivariate functional data. Advances in Data Analysis and Classification, 8(3), 321–338. doi:10.1007/s11634-014-0166-6
dc.identifier.issn18625347
dc.identifier.doi10.1007/s11634-014-0166-6
dc.identifier.urihttp://hdl.handle.net/10754/563434
dc.description.abstractWe propose notions of simplicial band depth for multivariate functional data that extend the univariate functional band depth. The proposed simplicial band depths provide simple and natural criteria to measure the centrality of a trajectory within a sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation study shows the robustness of this new definition of depth and the advantages of using a multivariate depth versus the marginal depths for detecting outliers. Real data examples from growth curves and signature data are used to illustrate the performance and usefulness of the proposed depths. © 2014 Springer-Verlag Berlin Heidelberg.
dc.publisherSpringer Nature
dc.subjectBand depth
dc.subjectFunctional and image data
dc.subjectFunctional boxplot
dc.subjectModified band depth
dc.subjectMultivariate
dc.subjectSimplicial
dc.titleSimplicial band depth for multivariate functional data
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.identifier.journalAdvances in Data Analysis and Classification
dc.contributor.institutionDepartment of Biostatistics, Columbia University, NY, NY, 10032, United States
dc.contributor.institutionDepartment of Statistics, The Ohio State University, Columbus, OH, 43210, United States
dc.contributor.institutionSearchForce, Inc., San Mateo, CA, 94403, United States
kaust.personGenton, Marc G.
dc.date.published-online2014-03-05
dc.date.published-print2014-09


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