The Shkarofsky-Gneiting class of covariance models for bivariate Gaussian random fields

Emilio Porcu, Moreno Bevilacqua, Amanda S. Hering

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose new covariance functions for bivariate Gaussian random fields that are very general and include as special cases other popular models proposed in earlier literature, namely, the bivariate Matérn and bivariate Cauchy models. The proposed model allows the covariance margins to belong to different parametric families with. To our knowledge, this is the first model of this type to be proposed in the literature. For instance, one of the margins can be of the Matérn type, whereas the latter can index long-range dependence. Estimation of the model is illustrated through simulation.

Original languageEnglish
Article numbere207
JournalStat
Volume7
Issue number1
DOIs
StatePublished - 2018
Externally publishedYes

Keywords

  • Matérn
  • bivariate covariance functions
  • generalized Cauchy
  • long-range dependence
  • smoothness

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