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ArticleAuthors
Krupskiy, PavelGenton, Marc G.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2018-09-24Online Publication Date
2018-09-24Print Publication Date
2019-01Permanent link to this record
http://hdl.handle.net/10754/628852
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Show full item recordAbstract
We propose a new copula model for replicated multivariate spatial data. Unlike classical models that assume multivariate normality of the data, the proposed copula is based on the assumption that some factors exist that affect the joint spatial dependence of all measurements of each variable as well as the joint dependence among these variables. The model is parameterized in terms of a cross-covariance function that may be chosen from the many models proposed in the literature. In addition, there are additive factors in the model that allow tail dependence and reflection asymmetry of each variable measured at different locations, and of different variables to be modeled. The proposed approach can therefore be seen as an extension of the linear model of coregionalization widely used for modeling multivariate spatial data. The likelihood of the model can be obtained in a simple form and, therefore, the likelihood estimation is quite fast. The model is not restricted to the set of data locations, and using the estimated copula, spatial data can be interpolated at locations where values of variables are unknown. We apply the proposed model to temperature and pressure data, and we compare its performance with that of a popular model from multivariate geostatistics.Citation
Krupskii P, Genton MG (2018) A copula model for non-Gaussian multivariate spatial data. Journal of Multivariate Analysis. Available: http://dx.doi.org/10.1016/j.jmva.2018.09.007.Sponsors
This research was supported by the King Abdullah University of Science and Technology (KAUST) . The authors would like to thank the associate editor and external referee for their constructive comments that led to an improved presentation.Publisher
Elsevier BVJournal
Journal of Multivariate AnalysisarXiv
1603.03950Additional Links
https://www.sciencedirect.com/science/article/pii/S0047259X18301696ae974a485f413a2113503eed53cd6c53
10.1016/j.jmva.2018.09.007