Covariance functions for multivariate Gaussian fields evolving temporally over planet earth
Impacto
Scholar |
Otros documentos de la autoría: Alegría, Alfredo; Porcu, Emilio; Furrer, Reinhard; Mateu, Jorge
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
comunitat-uji-handle4:
INVESTIGACIONEste recurso está restringido
https://doi.org/10.1007/s00477-019-01707-w |
Metadatos
Título
Covariance functions for multivariate Gaussian fields evolving temporally over planet earthFecha de publicación
2019-07-18Editor
Springer-Verlag GmbH Germany, part of Springer NatureISSN
1436-3259; 1436-3240Cita bibliográfica
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-wTipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-019-01707-wVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, Vol. 33 (2019)Entidad financiadora
Universidad Técnica Federico Santa María, Valparaiso, Chile | Swiss National Science Foundation
Código del proyecto o subvención
CONICYT-PCHA/Doctorado Nacional/2016-21160371 | 1130647 | SNSF-144973 | SNSF-175529 | MTM2016-78917-R
Título del proyecto o subvención
Proyecto Fondecyt Regular
Derechos de acceso
© 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
Aparece en las colecciones
- MAT_Articles [761]
- INIT_Articles [751]