Aggregation-cokriging for highly multivariate spatial data

dc.contributor.authorFurrer, R.
dc.contributor.authorGenton, M. G.
dc.contributor.institutionUniversitat Zurich, Zurich, Switzerland
dc.contributor.institutionTexas A and M University, College Station, United States
dc.date.accessioned2016-02-25T12:40:31Z
dc.date.available2016-02-25T12:40:31Z
dc.date.issued2011-08-26
dc.date.published-online2011-08-26
dc.date.published-print2011-09-01
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.
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.
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.
dc.identifier.doi10.1093/biomet/asr029
dc.identifier.issn0006-3444
dc.identifier.issn1464-3510
dc.identifier.journalBiometrika
dc.identifier.urihttp://hdl.handle.net/10754/597478
dc.publisherOxford University Press (OUP)
dc.subjectClimate
dc.subjectCokriging
dc.subjectEigendecomposition
dc.subjectIntrinsic process
dc.subjectLinear unbiased prediction
dc.titleAggregation-cokriging for highly multivariate spatial data
dc.typeArticle
display.details.left<span><h5>Type</h5>Article<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Furrer, R.,equals">Furrer, R.</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Genton, M. G.,equals">Genton, M. G.</a><br><br><h5>Online Publication Date</h5>2011-08-26<br><br><h5>Print Publication Date</h5>2011-09-01<br><br><h5>Date</h5>2011-08-26</span>
display.details.right<span><h5>Abstract</h5>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.<br><br><h5>Citation</h5>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.<br><br><h5>Acknowledgements</h5>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.<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Oxford University Press (OUP),equals">Oxford University Press (OUP)</a><br><br><h5>Journal</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.journal=Biometrika,equals">Biometrika</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1093/biomet/asr029">10.1093/biomet/asr029</a></span>
orcid.authorFurrer, R.
orcid.authorGenton, M. G.
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