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dc.contributor.authorQadir, Ghulam A.
dc.contributor.authorSun, Ying
dc.date.accessioned2019-12-18T12:03:39Z
dc.date.available2019-12-18T12:03:39Z
dc.date.issued2019-11-06
dc.identifier.urihttp://hdl.handle.net/10754/660675.1
dc.description.abstractThe prevalence of spatially referenced multivariate data has impelled researchers to develop a procedure for the joint modeling of multiple spatial processes. This ordinarily involves modeling marginal and cross-process dependence for any arbitrary pair of locations using a multivariate spatial covariance function. However, building a flexible multivariate spatial covariance function that is nonnegative definite is challenging. Here, we propose a semiparametric approach for multivariate spatial covariance function estimation with approximate Mat\'ern marginals and highly flexible cross-covariance functions via their spectral representations. The flexibility in our cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows us to capture non-trivial cross-spectral features. We then develop a likelihood-based estimation procedure and perform multiple simulation studies to demonstrate the performance of our method, especially on the coherence function estimation. Finally, we analyze particulate matter concentrations ($\text{PM}_{2.5}$) and wind speed data over the North-Eastern region of the United States, where we illustrate that our proposed method outperforms the commonly used full bivariate Mat\'ern model and the linear model of coregionalization for spatial prediction.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1911.02258
dc.rightsArchived with thanks to arXiv
dc.titleSemiparametric Estimation of Cross-covariance Functions for Multivariate Random Fields
dc.typePreprint
dc.contributor.departmentCEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.identifier.arxivid1911.02258
kaust.personQadir, Ghulam A.
kaust.personSun, Ying
refterms.dateFOA2019-12-18T12:04:22Z


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