Composite Likelihood Inference for Multivariate Gaussian Random Fields

Moreno Bevilacqua, Alfredo Alegria, Daira Velandia, Emilio Porcu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both the marginal and the cross-spatial dependence of the components of the associated multivariate Gaussian random field. However, effective estimation methods for these models are somehow unexplored. Maximum likelihood is certainly a useful tool, but it is impractical in all the circumstances where the number of observations is very large. In this work, we consider two possible approaches based on composite likelihood for multivariate covariance model estimation. We illustrate, through simulation experiments, that our methods offer a good balance between statistical efficiency and computational complexity. Asymptotic properties of the proposed estimators are assessed under increasing domain asymptotics. Finally, we apply the method for the analysis of a bivariate dataset on chlorophyll concentration and sea surface temperature in the Chilean coast.

Original languageEnglish
Pages (from-to)448-469
Number of pages22
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number3
StatePublished - 1 Sep 2016


  • Cross-covariance
  • Geostatistics
  • Large datasets


Dive into the research topics of 'Composite Likelihood Inference for Multivariate Gaussian Random Fields'. Together they form a unique fingerprint.

Cite this