Multivariate Kalman filtering for spatio-temporal processes
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https://doi.org/10.1007/s00477-022-02266-3 |
Metadatos
Título
Multivariate Kalman filtering for spatio-temporal processesFecha de publicación
2022Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliográfica
FERREIRA, Guillermo; MATEU, Jorge; PORCU, Emilio. Multivariate Kalman filtering for spatio-temporal processes. Stochastic Environmental Research and Risk Assessment, 2022, p. 1-18Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-022-02266-3Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
An increasing interest in models for multivariate spatio-temporal processes has been noted in the last years. Some of these models are very flexible and can capture both marginal and cross spatial associations amongst ... [+]
An increasing interest in models for multivariate spatio-temporal processes has been noted in the last years. Some of these models are very flexible and can capture both marginal and cross spatial associations amongst the components of the multivariate process. In order to contribute to the statistical analysis of these models, this paper deals with the estimation and prediction of multivariate spatio-temporal processes by using multivariate state-space models. In this context, a multivariate spatio-temporal process is represented through the well-known Wold decomposition. Such an approach allows for an easy implementation of the Kalman filter to estimate linear temporal processes exhibiting both short and long range dependencies, together with a spatial correlation structure. We illustrate, through simulation experiments, that our method offers a good balance between statistical efficiency and computational complexity. Finally, we apply the method for the analysis of a bivariate dataset on average daily temperatures and maximum daily solar radiations from 21 meteorological stations located in a portion of south-central Chile. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, 2022, p. 1-18Entidad financiadora
Universidad de Concepción | Center for the Discovery of Structures in Complex Data (MiDas) | Ministerio de Economía y Competitividad | University Jaume I
Código del proyecto o subvención
ENLACE 2018.014.028-1 | MTM2016-78917-R | UJI-B2018-04
Derechos de acceso
©The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
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