Covariance functions for multivariate Gaussian fields evolving temporally over planet earth
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Títol
Covariance functions for multivariate Gaussian fields evolving temporally over planet earthData de publicació
2019-07-18Editor
Springer-Verlag GmbH Germany, part of Springer NatureISSN
1436-3259; 1436-3240Cita bibliogràfica
Alegría, A., Porcu, E., Furrer, R. et al. Covariance functions for multivariate Gaussian fields evolving temporally over planet earth. Stoch Environ Res Risk Assess 33, 1593–1608 (2019). https://doi.org/10.1007/s00477-019-01707-wTipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://link.springer.com/article/10.1007/s00477-019-01707-wVersió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
The construction of valid and flexible cross-covariance functions is a fundamental task for modeling multivariate space–
time data arising from, e.g., climatological and oceanographical phenomena. Indeed, a suitable ... [+]
The construction of valid and flexible cross-covariance functions is a fundamental task for modeling multivariate space–
time data arising from, e.g., climatological and oceanographical phenomena. Indeed, a suitable specification of the
covariance structure allows to capture both the space–time dependencies between the observations and the development of
accurate predictions. For data observed over large portions of planet earth it is necessary to take into account the curvature
of the planet. Hence the need for random field models defined over spheres across time. In particular, the associated
covariance function should depend on the geodesic distance, which is the most natural metric over the spherical surface. In
this work, we propose a flexible parametric family of matrix-valued covariance functions, with both marginal and cross
structure being of the Gneiting type. We also introduce a different multivariate Gneiting model based on the adaptation of
the latent dimension approach to the spherical context. Finally, we assess the performance of our models through the study
of a bivariate space–time data set of surface air temperatures and precipitable water content. [-]
Publicat a
Stochastic Environmental Research and Risk Assessment, Vol. 33 (2019)Entitat finançadora
Universidad Técnica Federico Santa María, Valparaiso, Chile | Swiss National Science Foundation
Codi del projecte o subvenció
CONICYT-PCHA/Doctorado Nacional/2016-21160371 | 1130647 | SNSF-144973 | SNSF-175529 | MTM2016-78917-R
Títol del projecte o subvenció
Proyecto Fondecyt Regular
Drets d'accés
© Springer-Verlag GmbH Germany, part of Springer Nature 2019
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
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