Covariance tapering for multivariate Gaussian random fields estimation

M. Bevilacqua, A. Fassò, C. Gaetan, E. Porcu, D. Velandia

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

16 Citas (Scopus)

Resumen

In recent literature there has been a growing interest in the construction of covariance models for multivariate Gaussian random fields. However, effective estimation methods for these models are somehow unexplored. The maximum likelihood method has attractive features, but when we deal with large data sets this solution becomes impractical, so computationally efficient solutions have to be devised. In this paper we explore the use of the covariance tapering method for the estimation of multivariate covariance models. In particular, through a simulation study, we compare the use of simple separable tapers with more flexible multivariate tapers recently proposed in the literature and we discuss the asymptotic properties of the method under increasing domain asymptotics.

Idioma originalInglés
Páginas (desde-hasta)21-37
Número de páginas17
PublicaciónStatistical Methods and Applications
Volumen25
N.º1
DOI
EstadoPublicada - 1 mar. 2016
Publicado de forma externa

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