Spatially varying cross-correlation coefficients in the presence of nugget effects

Handle URI:
http://hdl.handle.net/10754/562426
Title:
Spatially varying cross-correlation coefficients in the presence of nugget effects
Authors:
Kleiber, William; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
We derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contraction matrix, yielding greater flexibility but added model complexity. We illustrate the approach with a bivariate minimum and maximum temperature dataset in Colorado, allowing the two variables to be positively correlated at low elevations and nearly independent at high elevations, while still yielding a positive definite covariance matrix. © 2012 Biometrika Trust.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Spatio-Temporal Statistics and Data Analysis Group
Publisher:
Oxford University Press (OUP)
Journal:
Biometrika
Issue Date:
29-Nov-2012
DOI:
10.1093/biomet/ass057
Type:
Article
ISSN:
00063444
Sponsors:
This research was partially supported by an award made by the King Abdullah University of Science and Technology and by the National Science Foundation.
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKleiber, Williamen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-08-03T10:37:48Zen
dc.date.available2015-08-03T10:37:48Zen
dc.date.issued2012-11-29en
dc.identifier.issn00063444en
dc.identifier.doi10.1093/biomet/ass057en
dc.identifier.urihttp://hdl.handle.net/10754/562426en
dc.description.abstractWe derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contraction matrix, yielding greater flexibility but added model complexity. We illustrate the approach with a bivariate minimum and maximum temperature dataset in Colorado, allowing the two variables to be positively correlated at low elevations and nearly independent at high elevations, while still yielding a positive definite covariance matrix. © 2012 Biometrika Trust.en
dc.description.sponsorshipThis research was partially supported by an award made by the King Abdullah University of Science and Technology and by the National Science Foundation.en
dc.publisherOxford University Press (OUP)en
dc.subjectCross-correlationen
dc.subjectMultivariate processen
dc.subjectNonstationarityen
dc.subjectRandom fielden
dc.subjectSpatial statisticen
dc.titleSpatially varying cross-correlation coefficients in the presence of nugget effectsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Groupen
dc.identifier.journalBiometrikaen
dc.contributor.institutionDepartment of Applied Mathematics, University of Colorado, Boulder, CO 80309, United Statesen
kaust.authorGenton, Marc G.en
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