Space-time autoregressive estimation and prediction with missing data based on Kalman filtering
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Otros documentos de la autoría: Padilla-Solis, Leonardo Fabricio; Lagos Álvarez, Bernardo M.; Mateu, Jorge; Porcu, Emilio
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https://doi.org/10.1002/env.2627 |
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Título
Space-time autoregressive estimation and prediction with missing data based on Kalman filteringFecha de publicación
2020-11Editor
WileyCita bibliográfica
PADILLA, Leonardo, et al. Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering. Environmetrics, 2020, p. e2627.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2627Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
We propose a Kalman filter algorithm to provide a formal statistical analysis of space‐time data with an autoregressive structure in time. The Kalman filter technique allows to capture the temporal dependence as well ... [+]
We propose a Kalman filter algorithm to provide a formal statistical analysis of space‐time data with an autoregressive structure in time. The Kalman filter technique allows to capture the temporal dependence as well as the spatial correlation structure through state‐space equations, and it is aimed to perform statistical inference in terms of parameter estimation and prediction at unobserved locations. We thus develop space‐time estimation and prediction methods in the presence of missing data, through the Kalman filter, in order to obtain accurate estimates of model parameters and reliable space‐time predictions. Our findings are illustrated through an application on daily air temperatures in some regions of southern Chile, where the dataset shows a number of missing data in many locations. [-]
Publicado en
Environmetrics, 2020, v. 31, issue 7Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
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