Show simple item record

dc.contributor.authorChen, Wanfang
dc.contributor.authorGenton, Marc G.
dc.contributor.authorSun, Ying
dc.date.accessioned2021-02-08T06:29:46Z
dc.date.available2021-02-08T06:29:46Z
dc.date.issued2020-10-16
dc.date.submitted2020-10-16
dc.identifier.citationChen, W., Genton, M. G., & Sun, Y. (2020). Space-Time Covariance Structures and Models. Annual Review of Statistics and Its Application, 8(1). doi:10.1146/annurev-statistics-042720-115603
dc.identifier.issn2326-8298
dc.identifier.issn2326-831X
dc.identifier.doi10.1146/annurev-statistics-042720-115603
dc.identifier.urihttp://hdl.handle.net/10754/667271
dc.description.abstractIn recent years, interest has grown in modeling spatio-temporal data generated from monitoring networks, satellite imaging, and climate models. Under Gaussianity, the covariance function is core to spatio-temporal modeling, inference, and prediction. In this article, we review the various space-time covariance structures in which simplified assumptions, such as separability and full symmetry, are made to facilitate computation, and associated tests intended to validate these structures. We also review recent developments on constructing space-time covariance models, which can be separable or nonseparable, fully symmetric or asymmetric, stationary or nonstationary, univariate or multivariate, and in Euclidean spaces or on the sphere. We visualize some of the structures and models with visuanimations. Finally, we discuss inference for fitting space-time covariance models and describe a case study based on a new wind-speed data set. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
dc.description.sponsorshipThis publication is based on research supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3742.
dc.publisherAnnual Reviews
dc.relation.urlhttps://www.annualreviews.org/doi/10.1146/annurev-statistics-042720-115603
dc.rightsArchived with thanks to Annual Review of Statistics and Its Application
dc.titleSpace-Time Covariance Structures and Models
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics Program
dc.identifier.journalAnnual Review of Statistics and Its Application
dc.rights.embargodate2021-10-16
dc.eprint.versionPost-print
dc.identifier.volume8
dc.identifier.issue1
kaust.personChen, Wanfang
kaust.personGenton, Marc G.
kaust.personSun, Ying
dc.embargo2021-10-16
dc.embargo2021-10-16
kaust.grant.numberOSR-2018-CRG7-3742.
refterms.dateFOA2021-02-08T12:49:22Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


Files in this item

Thumbnail
Name:
1Template-for-Authors-ChenGenSunRev2-2 (4).pdf
Size:
471.7Kb
Format:
PDF
Description:
Accepted manuscript
Embargo End Date:
2021-10-16

This item appears in the following Collection(s)

Show simple item record