Show simple item record

dc.contributor.authorPorcu, Emilio
dc.contributor.authorBevilacqua, Moreno
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2020-01-16T08:48:14Z
dc.date.available2020-01-16T08:48:14Z
dc.date.issued2020
dc.date.submitted2019
dc.identifier.doi10.5705/ss.202017.0385
dc.identifier.urihttp://hdl.handle.net/10754/661056
dc.description.abstractThe paper provides new classes of nonseparable space-time covariance functions with spatial (or temporal) margin belonging to the Generalized Wendland class of compactly supported covariance functions. An interesting feature of our covariances, from the computational viewpoint, is that the compact support is a decreasing function of the temporal (spatial) lag. We provide conditions for the validity of the proposed class, and analyze the problem of differentiability at the origin for the temporal (spatial) margin. A simulation study explores the finite sample properties and the computational burden associated with the maximum likelihood estimation of the covariance parameters. Finally, we use the proposed covariance models on Irish wind speed data and compare them with Gneiting-Mat´ern models in terms of fitting, prediction efficiency and computational burden. Necessary and sufficient conditions together with other results on dynamically varying compact supports are provided in the Online Supplement to this paper.
dc.description.sponsorshipThe research work conducted by Moreno Bevilacqua was supported in part by FONDECYT grant 1160280 Chile. Emilio Porcu was partly supported by FONDECYT 1130647 Chile. The work of Marc G. Genton was supported by King Abdullah University of Science and Technology (KAUST).
dc.language.isoen
dc.publisherAcademia Sinica, Institute of Statistical Science
dc.rightsArchived with thanks to Statistica Sinica
dc.titleNonseparable, Space-Time Covariance Functions with Dynamical Compact Supports
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics Program
dc.identifier.journalStatistica Sinica
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Mathematics, Statistics, and Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
dc.contributor.institutionUniversidad de Valparaiso, Department of Statistics, 2360102 Valparaiso, Chile.
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
pubs.publication-statusAccepted
kaust.personGenton, Marc G.
refterms.dateFOA2020-01-16T08:48:15Z


Files in this item

Thumbnail
Name:
SS-Revision-Final.pdf
Size:
298.5Kb
Format:
PDF
Description:
Accepted manuscript

This item appears in the following Collection(s)

Show simple item record