Cross-covariance functions for multivariate random fields based on latent dimensions

Handle URI:
http://hdl.handle.net/10754/597897
Title:
Cross-covariance functions for multivariate random fields based on latent dimensions
Authors:
Apanasovich, T. V.; Genton, M. G.
Abstract:
The problem of constructing valid parametric cross-covariance functions is challenging. We propose a simple methodology, based on latent dimensions and existing covariance models for univariate random fields, to develop flexible, interpretable and computationally feasible classes of cross-covariance functions in closed form. We focus on spatio-temporal cross-covariance functions that can be nonseparable, asymmetric and can have different covariance structures, for instance different smoothness parameters, in each component. We discuss estimation of these models and perform a small simulation study to demonstrate our approach. We illustrate our methodology on a trivariate spatio-temporal pollution dataset from California and demonstrate that our cross-covariance performs better than other competing models. © 2010 Biometrika Trust.
Citation:
Apanasovich TV, Genton MG (2010) Cross-covariance functions for multivariate random fields based on latent dimensions. Biometrika 97: 15–30. Available: http://dx.doi.org/10.1093/biomet/asp078.
Publisher:
Oxford University Press (OUP)
Journal:
Biometrika
Issue Date:
16-Feb-2010
DOI:
10.1093/biomet/asp078
Type:
Article
ISSN:
0006-3444; 1464-3510
Sponsors:
The authors are grateful to the editor, an associate editor and two anonymous referees for theirvaluable comments. This research was sponsored by the National Science Foundation, U.S.A.,and by an award made by the King Abdullah University of Science and Technology
Appears in Collections:
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Full metadata record

DC FieldValue Language
dc.contributor.authorApanasovich, T. V.en
dc.contributor.authorGenton, M. G.en
dc.date.accessioned2016-02-25T12:58:32Zen
dc.date.available2016-02-25T12:58:32Zen
dc.date.issued2010-02-16en
dc.identifier.citationApanasovich TV, Genton MG (2010) Cross-covariance functions for multivariate random fields based on latent dimensions. Biometrika 97: 15–30. Available: http://dx.doi.org/10.1093/biomet/asp078.en
dc.identifier.issn0006-3444en
dc.identifier.issn1464-3510en
dc.identifier.doi10.1093/biomet/asp078en
dc.identifier.urihttp://hdl.handle.net/10754/597897en
dc.description.abstractThe problem of constructing valid parametric cross-covariance functions is challenging. We propose a simple methodology, based on latent dimensions and existing covariance models for univariate random fields, to develop flexible, interpretable and computationally feasible classes of cross-covariance functions in closed form. We focus on spatio-temporal cross-covariance functions that can be nonseparable, asymmetric and can have different covariance structures, for instance different smoothness parameters, in each component. We discuss estimation of these models and perform a small simulation study to demonstrate our approach. We illustrate our methodology on a trivariate spatio-temporal pollution dataset from California and demonstrate that our cross-covariance performs better than other competing models. © 2010 Biometrika Trust.en
dc.description.sponsorshipThe authors are grateful to the editor, an associate editor and two anonymous referees for theirvaluable comments. This research was sponsored by the National Science Foundation, U.S.A.,and by an award made by the King Abdullah University of Science and Technologyen
dc.publisherOxford University Press (OUP)en
dc.subjectAsymmetryen
dc.subjectLinear model of coregionalizationen
dc.subjectNonseparabilityen
dc.subjectPositive definitenessen
dc.subjectSpace and timeen
dc.subjectStationarityen
dc.titleCross-covariance functions for multivariate random fields based on latent dimensionsen
dc.typeArticleen
dc.identifier.journalBiometrikaen
dc.contributor.institutionThomas Jefferson University, Philadelphia, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
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