Covariance tapering for multivariate Gaussian random fields estimation

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

Statistical Methods & Applications, 2016, vol. 25, issue 1, 37 pages

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. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Cross Covariance estimation; Large datasets; Multivariate compactly supported correlation function; Multivariate Gaussian process (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10260-015-0338-3

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