TY - JOUR
T1 - Cross-covariance functions for multivariate random fields based on latent dimensions
AU - Apanasovich, T. V.
AU - Genton, M. G.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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 Technology
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2010/2/16
Y1 - 2010/2/16
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/597897
UR - https://academic.oup.com/biomet/article-lookup/doi/10.1093/biomet/asp078
UR - http://www.scopus.com/inward/record.url?scp=77249111193&partnerID=8YFLogxK
U2 - 10.1093/biomet/asp078
DO - 10.1093/biomet/asp078
M3 - Article
AN - SCOPUS:77249111193
VL - 97
SP - 15
EP - 30
JO - Biometrika
JF - Biometrika
SN - 0006-3444
IS - 1
ER -