Handle URI:
http://hdl.handle.net/10754/597478
Title:
Aggregation-cokriging for highly multivariate spatial data
Authors:
Furrer, R.; Genton, M. G.
Abstract:
Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.
Citation:
Furrer R, Genton MG (2011) Aggregation-cokriging for highly multivariate spatial data. Biometrika 98: 615–631. Available: http://dx.doi.org/10.1093/biomet/asr029.
Publisher:
Oxford University Press (OUP)
Journal:
Biometrika
Issue Date:
26-Aug-2011
DOI:
10.1093/biomet/asr029
Type:
Article
ISSN:
0006-3444; 1464-3510
Sponsors:
This research was sponsored by the National Science Foundation, U.S.A., and by an awardmade by the King Abdullah University of Science and Technology. We acknowledge the internationalmodelling groups for providing their data for analysis. We also thank the editor, theassociate editor and two referees for comments that led to a substantial improvement of the paper.
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DC FieldValue Language
dc.contributor.authorFurrer, R.en
dc.contributor.authorGenton, M. G.en
dc.date.accessioned2016-02-25T12:40:31Zen
dc.date.available2016-02-25T12:40:31Zen
dc.date.issued2011-08-26en
dc.identifier.citationFurrer R, Genton MG (2011) Aggregation-cokriging for highly multivariate spatial data. Biometrika 98: 615–631. Available: http://dx.doi.org/10.1093/biomet/asr029.en
dc.identifier.issn0006-3444en
dc.identifier.issn1464-3510en
dc.identifier.doi10.1093/biomet/asr029en
dc.identifier.urihttp://hdl.handle.net/10754/597478en
dc.description.abstractBest linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.en
dc.description.sponsorshipThis research was sponsored by the National Science Foundation, U.S.A., and by an awardmade by the King Abdullah University of Science and Technology. We acknowledge the internationalmodelling groups for providing their data for analysis. We also thank the editor, theassociate editor and two referees for comments that led to a substantial improvement of the paper.en
dc.publisherOxford University Press (OUP)en
dc.subjectClimateen
dc.subjectCokrigingen
dc.subjectEigendecompositionen
dc.subjectIntrinsic processen
dc.subjectLinear unbiased predictionen
dc.titleAggregation-cokriging for highly multivariate spatial dataen
dc.typeArticleen
dc.identifier.journalBiometrikaen
dc.contributor.institutionUniversitat Zurich, Zurich, Switzerlanden
dc.contributor.institutionTexas A and M University, College Station, United Statesen
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