Simplicial band depth for multivariate functional data
dc.contributor.author | López-Pintado, Sara | |
dc.contributor.author | Sun, Ying | |
dc.contributor.author | Lin, Juan K. | |
dc.contributor.author | Genton, Marc G. | |
dc.date.accessioned | 2015-08-03T11:51:22Z | |
dc.date.available | 2015-08-03T11:51:22Z | |
dc.date.issued | 2014-03-05 | |
dc.identifier.citation | López-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.issn | 18625347 | |
dc.identifier.doi | 10.1007/s11634-014-0166-6 | |
dc.identifier.uri | http://hdl.handle.net/10754/563434 | |
dc.description.abstract | We 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.publisher | Springer Nature | |
dc.subject | Band depth | |
dc.subject | Functional and image data | |
dc.subject | Functional boxplot | |
dc.subject | Modified band depth | |
dc.subject | Multivariate | |
dc.subject | Simplicial | |
dc.title | Simplicial band depth for multivariate functional data | |
dc.type | Article | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Applied Mathematics and Computational Science Program | |
dc.contributor.department | Spatio-Temporal Statistics and Data Analysis Group | |
dc.identifier.journal | Advances in Data Analysis and Classification | |
dc.contributor.institution | Department of Biostatistics, Columbia University, NY, NY, 10032, United States | |
dc.contributor.institution | Department of Statistics, The Ohio State University, Columbus, OH, 43210, United States | |
dc.contributor.institution | SearchForce, Inc., San Mateo, CA, 94403, United States | |
kaust.person | Genton, Marc G. | |
dc.date.published-online | 2014-03-05 | |
dc.date.published-print | 2014-09 |
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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
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