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Article: Time Varying Spatio-temporal Covariance Models

TitleTime Varying Spatio-temporal Covariance Models
Authors
Issue Date2015
PublisherElsevier BV. The Journal's web site is located at http://www.journals.elsevier.com/spatial-statistics
Citation
Spatial Statistics, 2015, v. 14 n. Part C, p. 269-285 How to Cite?
AbstractIn this paper, we introduce valid parametric covariance models for univariate and multivariate spatio-temporal random fields. In contrast to the traditional models, we allow the model parameters to vary over time. Since variables in applications usually exhibit seasonality or changes in dependency structures, the allowance of varying parameters would be beneficial in terms of improving model flexibility. Conditions in constructing valid covariance models and discussions on practical implementation will be provided. As an application, a set of air pollution data observed from a monitoring network will be modeled. It is found that the time varying model performs better in prediction compared with the traditional models. © 2015 Elsevier Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/222906
ISSN
2015 Impact Factor: 1.385
2015 SCImago Journal Rankings: 1.052
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorIP, RHL-
dc.contributor.authorLi, WK-
dc.date.accessioned2016-02-12T06:23:12Z-
dc.date.available2016-02-12T06:23:12Z-
dc.date.issued2015-
dc.identifier.citationSpatial Statistics, 2015, v. 14 n. Part C, p. 269-285-
dc.identifier.issn2211-6753-
dc.identifier.urihttp://hdl.handle.net/10722/222906-
dc.description.abstractIn this paper, we introduce valid parametric covariance models for univariate and multivariate spatio-temporal random fields. In contrast to the traditional models, we allow the model parameters to vary over time. Since variables in applications usually exhibit seasonality or changes in dependency structures, the allowance of varying parameters would be beneficial in terms of improving model flexibility. Conditions in constructing valid covariance models and discussions on practical implementation will be provided. As an application, a set of air pollution data observed from a monitoring network will be modeled. It is found that the time varying model performs better in prediction compared with the traditional models. © 2015 Elsevier Ltd.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.journals.elsevier.com/spatial-statistics-
dc.relation.ispartofSpatial Statistics-
dc.titleTime Varying Spatio-temporal Covariance Models-
dc.typeArticle-
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hk-
dc.identifier.authorityLi, WK=rp00741-
dc.identifier.doi10.1016/j.spasta.2015.06.006-
dc.identifier.scopuseid_2-s2.0-84949314782-
dc.identifier.hkuros256877-
dc.identifier.volume14-
dc.identifier.issuePart C-
dc.identifier.spage269-
dc.identifier.epage285-
dc.identifier.isiWOS:000368913400004-
dc.publisher.placeNetherlands-

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