Statistical Methods & Applications

, Volume 25, Issue 1, pp 21–37

Covariance tapering for multivariate Gaussian random fields estimation

  • M. Bevilacqua
  • A. Fassò
  • C. Gaetan
  • E. Porcu
  • D. Velandia
Original Paper

DOI: 10.1007/s10260-015-0338-3

Cite this article as:
Bevilacqua, M., Fassò, A., Gaetan, C. et al. Stat Methods Appl (2016) 25: 21. doi:10.1007/s10260-015-0338-3

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.

Keywords

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

Supplementary material

10260_2015_338_MOESM1_ESM.pdf (217 kb)
Supplementary material 1 (pdf 217 KB)

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • M. Bevilacqua
    • 1
  • A. Fassò
    • 2
  • C. Gaetan
    • 3
  • E. Porcu
    • 4
  • D. Velandia
    • 1
  1. 1.Instituto de EstadísticaUniversidad de ValparaísoValparaisoChile
  2. 2.Dipartimento di IngegneriaUniversitá degli Studi di BergamoBergamoItaly
  3. 3.DAIS, Universitá Cá Foscari-VeneziaVeniceItaly
  4. 4.Departamento de MatemáticaUniversidad Técnica Federico Santa MaríaValparaisoChile