Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach
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Otros documentos de la autoría: Bevilacqua, M.; Gaetan, Carlo; Mateu, Jorge; Porcu, Emilio
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Título
Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood ApproachFecha de publicación
2012Editor
American Statistical AssociationISSN
0162-1459Cita bibliográfica
BEVILACQUA, Moreno, et al. Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach. Journal of the American Statistical Association, 2012, vol. 107, no 497, p. 268-280Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2011.646928#.UcwrYJ-RFRkVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
In the last years there has been a growing interest in the construction space-time covariance functions. However, effective estimation methods for these models are some- how unexplored. In this paper we propose a ... [+]
In the last years there has been a growing interest in the construction space-time covariance functions. However, effective estimation methods for these models are some- how unexplored. In this paper we propose a composite likelihood approach and a weighted variant for the space-time estimation problem.
The proposed method can be a valid compromise between the computational bur- dens, induced by the use of a maximum likelihood approach, and the loss of efficiency induced by using a weighted least squares procedure. An identification criterion based on the composite likelihood is also introduced. The effectiveness of the proposed pro- cedure is illustrated through an extensive simulation experiment, and by reanalising a data set on Irish wind speeds (Haslett and Raftery, 1989). We also address an im- portant issue, which has been recently explored in the literature, on how to select an appropriate space-time model by accounting for the tradeoff between goodness-of-fit and model complexity. [-]
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Journal of the American Statistical Association, 107, 497Derechos de acceso
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info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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