Factor Copula Models for Replicated Spatial Data
Type
ArticleAuthors
Krupskii, PavelHuser, Raphaël
Genton, Marc G.
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Online Publication Date
2016-12-16Print Publication Date
2018-01-02Date
2016-12-16Abstract
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.Citation
Krupskii P, Huser R, Genton MG (2016) Factor Copula Models for Replicated Spatial Data. Journal of the American Statistical Association: 0–0. Available: http://dx.doi.org/10.1080/01621459.2016.1261712.Acknowledgements
This research was supported by the King Abdullah University of Science and Technology (KAUST).Publisher
Informa UK LimitedJournal
Journal of the American Statistical AssociationDOI
10.1080/01621459.2016.1261712arXiv
1511.03000Additional Links
http://www.tandfonline.com/doi/full/10.1080/01621459.2016.1261712Relations
Is Supplemented By:- [Dataset]
Krupskii, P., Huser, R., & Genton, M. G. (2016). Factor Copula Models for Replicated Spatial Data. Figshare. https://doi.org/10.6084/m9.figshare.4478411. DOI: 10.6084/m9.figshare.4478411 HANDLE: 10754/624778