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    A copula model for non-Gaussian multivariate spatial data

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    Type
    Article
    Authors
    Krupskiy, Pavel
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2018-09-24
    Online Publication Date
    2018-09-24
    Print Publication Date
    2019-01
    Permanent link to this record
    http://hdl.handle.net/10754/628852
    
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    Abstract
    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 BV
    Journal
    Journal of Multivariate Analysis
    DOI
    10.1016/j.jmva.2018.09.007
    arXiv
    1603.03950
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0047259X18301696
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jmva.2018.09.007
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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