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Lookup NU author(s): Professor Emilio Porcu
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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.
Author(s): Bevilacqua M, Fasso A, Gaetan C, Porcu E, Velandia D
Publication type: Article
Publication status: Published
Journal: Statistical Methods & Applications
Year: 2016
Volume: 25
Issue: 1
Pages: 21–37
Print publication date: 01/03/2016
Online publication date: 26/10/2015
Acceptance date: 07/10/2015
ISSN (print): 1618-2510
ISSN (electronic): 1613-981X
Publisher: Springer
URL: https://doi.org/10.1007/s10260-015-0338-3
DOI: 10.1007/s10260-015-0338-3
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