Nonstationary cross-covariance functions for multivariate spatio-temporal random fields
dc.contributor.author | Salvaña, Mary Lai O. | |
dc.contributor.author | Genton, Marc G. | |
dc.date.accessioned | 2020-01-26T08:56:59Z | |
dc.date.available | 2020-01-26T08:56:59Z | |
dc.date.issued | 2020-01-25 | |
dc.date.submitted | 2019-10-13 | |
dc.identifier.citation | Salvaña, M. L. O., & Genton, M. G. (2020). Nonstationary cross-covariance functions for multivariate spatio-temporal random fields. Spatial Statistics, 100411. doi:10.1016/j.spasta.2020.100411 | |
dc.identifier.doi | 10.1016/j.spasta.2020.100411 | |
dc.identifier.uri | http://hdl.handle.net/10754/661149 | |
dc.description.abstract | In multivariate spatio-temporal analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal cross-covariance function, either directly or through the construction of processes. This task is difficult as these functions should yield positive definite covariance matrices. In recent years, we have seen a flourishing of methods and theories on constructing spatio-temporal cross-covariance functions satisfying the positive definiteness requirement. A subset of those techniques produced spatio-temporal cross-covariance functions possessing the additional feature of nonstationarity. Here we provide a review of the state-of-the-art methods and technical progress regarding model construction. In addition, we introduce a rich class of multivariate spatio-temporal asymmetric nonstationary models stemming from the Lagrangian framework. We demonstrate the capabilities of the proposed models on a bivariate reanalysis climate model output dataset previously analyzed using purely spatial models. Furthermore, we carry out a cross-validation study to examine the advantages of using spatio-temporal models over purely spatial models. Finally, we outline future research directions and open problems. | |
dc.publisher | Elsevier BV | |
dc.relation.url | https://linkinghub.elsevier.com/retrieve/pii/S2211675320300051 | |
dc.rights | NOTICE: this is the author’s version of a work that was accepted for publication in Spatial Statistics. 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 Spatial Statistics, [[Volume], [Issue], (2020-01-25)] DOI: 10.1016/j.spasta.2020.100411 . © 2020. 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.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Nonstationary cross-covariance functions for multivariate spatio-temporal random fields | |
dc.type | Article | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Spatio-Temporal Statistics and Data Analysis Group | |
dc.contributor.department | Statistics Program | |
dc.identifier.journal | Spatial Statistics | |
dc.rights.embargodate | 2022-01-25 | |
dc.eprint.version | Post-print | |
kaust.person | Salvaña, Mary Lai O. | |
kaust.person | Genton, Marc G. | |
dc.date.accepted | 2020-01-16 | |
refterms.dateFOA | 2020-01-26T10:34:52Z | |
dc.date.published-online | 2020-01-25 | |
dc.date.published-print | 2020-01 |
Files in this item
This item appears in the following Collection(s)
-
Articles
-
Statistics Program
For more information visit: https://stat.kaust.edu.sa/ -
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
For more information visit: https://cemse.kaust.edu.sa/