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    Factor copula models for data with spatio-temporal dependence

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    Type
    Article
    Authors
    Krupskii, Pavel cc
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2017-10-13
    Online Publication Date
    2017-10-13
    Print Publication Date
    2017-11
    Permanent link to this record
    http://hdl.handle.net/10754/625889
    
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    Abstract
    We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.
    Citation
    Krupskii P, Genton MG (2017) Factor copula models for data with spatio-temporal dependence. Spatial Statistics. Available: http://dx.doi.org/10.1016/j.spasta.2017.10.001.
    Sponsors
    This research was supported by the King Abdullah University of Science and Technology (KAUST) .
    Publisher
    Elsevier BV
    Journal
    Spatial Statistics
    DOI
    10.1016/j.spasta.2017.10.001
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S2211675317300210
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.spasta.2017.10.001
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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