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dc.contributor.authorChen, Wanfang
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2019-08-19T10:43:05Z
dc.date.available2019-08-19T10:43:05Z
dc.date.issued2019-07-30
dc.identifier.citationChen, W., & Genton, M. G. (2019). Parametric variogram matrices incorporating both bounded and unbounded functions. Stochastic Environmental Research and Risk Assessment. doi:10.1007/s00477-019-01710-1
dc.identifier.doi10.1007/s00477-019-01710-1
dc.identifier.urihttp://hdl.handle.net/10754/656510
dc.description.abstractWe construct a flexible class of parametric models for both traditional and pseudo variogram matrix (valued functions), where the off-diagonal elements are the traditional cross variograms and pseudo cross variograms, respectively, and the diagonal elements are the direct variograms, based on the method of latent dimensions and the linear model of coregionalization. The entries in the parametric variogram matrix allow for a smooth transition between boundedness and unboundedness by changing the values of parameters, and thus between joint second-order and intrinsically stationary vector random fields, or between multivariate geometric Gaussian processes and multivariate Brown–Resnick processes in spatial extreme analysis.
dc.description.sponsorshipThe authors are grateful to Martin Schlather for providing the R code used in Schlather and Moreva (2017), based on which the visuanimations of direct and cross variograms in Movies 1 and 2 in the electronic supplementary material were produced. This research was supported by King Abdullah University of Science and Technology (KAUST).
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/10.1007/s00477-019-01710-1
dc.rightsArchived with thanks to Stochastic Environmental Research and Risk Assessment
dc.subjectBounded and unbounded variogram
dc.subjectCross variogram
dc.subjectIntrinsic stationarity
dc.subjectSecond-order stationarity
dc.subjectVariogram matrix
dc.titleParametric variogram matrices incorporating both bounded and unbounded functions
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalStochastic Environmental Research and Risk Assessment
dc.rights.embargodate2020-07-30
dc.eprint.versionPost-print
kaust.personChen, Wanfang
kaust.personGenton, Marc G.
refterms.dateFOA2020-07-30T00:00:00Z


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