Cross-covariance functions for multivariate random fields based on latent dimensions
Type
ArticleAuthors
Apanasovich, T. V.Genton, M. G.
Date
2010-02-16Online Publication Date
2010-02-16Print Publication Date
2010-03-01Permanent link to this record
http://hdl.handle.net/10754/597897
Metadata
Show full item recordAbstract
The problem of constructing valid parametric cross-covariance functions is challenging. We propose a simple methodology, based on latent dimensions and existing covariance models for univariate random fields, to develop flexible, interpretable and computationally feasible classes of cross-covariance functions in closed form. We focus on spatio-temporal cross-covariance functions that can be nonseparable, asymmetric and can have different covariance structures, for instance different smoothness parameters, in each component. We discuss estimation of these models and perform a small simulation study to demonstrate our approach. We illustrate our methodology on a trivariate spatio-temporal pollution dataset from California and demonstrate that our cross-covariance performs better than other competing models. © 2010 Biometrika Trust.Citation
Apanasovich TV, Genton MG (2010) Cross-covariance functions for multivariate random fields based on latent dimensions. Biometrika 97: 15–30. Available: http://dx.doi.org/10.1093/biomet/asp078.Sponsors
The authors are grateful to the editor, an associate editor and two anonymous referees for theirvaluable comments. This research was sponsored by the National Science Foundation, U.S.A.,and by an award made by the King Abdullah University of Science and TechnologyPublisher
Oxford University Press (OUP)Journal
Biometrikaae974a485f413a2113503eed53cd6c53
10.1093/biomet/asp078