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Article: Time Varying Spatio-temporal Covariance Models
Title | Time Varying Spatio-temporal Covariance Models |
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Authors | |
Keywords | Monitoring datasets Multivariate processes Prediction Valid covariance models |
Issue Date | 2015 |
Publisher | Elsevier 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/222906 |
ISSN | 2023 Impact Factor: 2.1 2023 SCImago Journal Rankings: 0.805 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | IP, RHL | - |
dc.contributor.author | Li, WK | - |
dc.date.accessioned | 2016-02-12T06:23:12Z | - |
dc.date.available | 2016-02-12T06:23:12Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Spatial Statistics, 2015, v. 14 n. Part C, p. 269-285 | - |
dc.identifier.issn | 2211-6753 | - |
dc.identifier.uri | http://hdl.handle.net/10722/222906 | - |
dc.description.abstract | In 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.language | eng | - |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.journals.elsevier.com/spatial-statistics | - |
dc.relation.ispartof | Spatial Statistics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Monitoring datasets | - |
dc.subject | Multivariate processes | - |
dc.subject | Prediction | - |
dc.subject | Valid covariance models | - |
dc.title | Time Varying Spatio-temporal Covariance Models | - |
dc.type | Article | - |
dc.identifier.email | Li, WK: hrntlwk@hkucc.hku.hk | - |
dc.identifier.authority | Li, WK=rp00741 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1016/j.spasta.2015.06.006 | - |
dc.identifier.scopus | eid_2-s2.0-84949314782 | - |
dc.identifier.hkuros | 256877 | - |
dc.identifier.volume | 14 | - |
dc.identifier.issue | Part C | - |
dc.identifier.spage | 269 | - |
dc.identifier.epage | 285 | - |
dc.identifier.isi | WOS:000368913400004 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 2211-6753 | - |