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ArticleAuthors
Krupskii, Pavel
Genton, Marc G.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2017-10-13Online Publication Date
2017-10-13Print Publication Date
2017-11Permanent link to this record
http://hdl.handle.net/10754/625889
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Show full item recordAbstract
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 BVJournal
Spatial StatisticsAdditional Links
http://www.sciencedirect.com/science/article/pii/S2211675317300210ae974a485f413a2113503eed53cd6c53
10.1016/j.spasta.2017.10.001