Covariance tapering for multivariate Gaussian random fields estimation

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

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish
Pages (from-to)21-37
Number of pages17
JournalStatistical Methods and Applications
Volume25
Issue number1
DOIs
StatePublished - 1 Mar 2016
Externally publishedYes

Keywords

  • Cross Covariance estimation
  • Large datasets
  • Multivariate Gaussian process
  • Multivariate compactly supported correlation function

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