Multivariate Kalman filtering for spatio-temporal processes
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https://doi.org/10.1007/s00477-022-02266-3 |
Metadades
Títol
Multivariate Kalman filtering for spatio-temporal processesData de publicació
2022Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliogràfica
FERREIRA, Guillermo; MATEU, Jorge; PORCU, Emilio. Multivariate Kalman filtering for spatio-temporal processes. Stochastic Environmental Research and Risk Assessment, 2022, p. 1-18Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://link.springer.com/article/10.1007/s00477-022-02266-3Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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. [-]
Publicat a
Stochastic Environmental Research and Risk Assessment, 2022, p. 1-18Entitat finançadora
Universidad de Concepción | Center for the Discovery of Structures in Complex Data (MiDas) | Ministerio de Economía y Competitividad | University Jaume I
Codi del projecte o subvenció
ENLACE 2018.014.028-1 | MTM2016-78917-R | UJI-B2018-04
Drets d'accés
©The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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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