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Covariance tapering for multivariate Gaussian random fields estimation

Lookup NU author(s): Professor Emilio Porcu

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Abstract

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.


Publication metadata

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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