Multivariate functional random fields: prediction and optimal sampling
Impacto
Scholar |
Otros documentos de la autoría: Bohorquez Castañeda, Martha Patricia; Giraldo, Ramón; Mateu, Jorge
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1007/s00477-016-1266-y |
Metadatos
Título
Multivariate functional random fields: prediction and optimal samplingFecha de publicación
2017Editor
Springer VerlagISSN
1436-3259; 1436-3240Cita bibliográfica
BOHORQUEZ, M.; GIRALDO, R.; MATEU, J. Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assessment, 2016, p. 1-18Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://link.springer.com/article/10.1007/s00477-016-1266-yVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper develops spatial prediction of a functional variable at unsampled sites, using functional covariates, that is, we present a functional cokriging method. We show that through the representation of each ... [+]
This paper develops spatial prediction of a functional variable at unsampled sites, using functional covariates, that is, we present a functional cokriging method. We show that through the representation of each function in terms of its empirical functional principal components, the functional cokriging only depends on the auto-covariance and cross-covariance of the associated scores vectors, which are scalar random fields. In addition, we propose the methodology to find optimal sampling designs in this context. The proposal is applied to the network of air quality in México city. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, 2016, vol. 31, n. 1Derechos de acceso
© Springer-Verlag Berlin Heidelberg 2016
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info:eu-repo/semantics/restrictedAccess
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