Covariance functions for multivariate Gaussian fields evolving temporally over planet earth
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Other documents of the author: Alegría, Alfredo; Porcu, Emilio; Furrer, Reinhard; Mateu, Jorge
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Title
Covariance functions for multivariate Gaussian fields evolving temporally over planet earthDate
2019-07-18Publisher
Springer-Verlag GmbH Germany, part of Springer NatureISSN
1436-3259; 1436-3240Bibliographic citation
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-wType
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s00477-019-01707-wVersion
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Abstract
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. [-]
Is part of
Stochastic Environmental Research and Risk Assessment, Vol. 33 (2019)Funder Name
Universidad Técnica Federico Santa María, Valparaiso, Chile | Swiss National Science Foundation
Project code
CONICYT-PCHA/Doctorado Nacional/2016-21160371 | 1130647 | SNSF-144973 | SNSF-175529 | MTM2016-78917-R
Project title or grant
Proyecto Fondecyt Regular
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© Springer-Verlag GmbH Germany, part of Springer Nature 2019
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