Comparing composite likelihood methods based on pairs for spatial Gaussian random fields

Moreno Bevilacqua, Carlo Gaetan

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when we deal with a Gaussian model. However maximum likelihood becomes impractical when the number of observations is very large. In this work we review some solutions and we contrast them in terms of loss of statistical efficiency and computational burden. Specifically we focus on three types of weighted composite likelihood functions based on pairs and we compare them with the method of covariance tapering. Asymptotic properties of the three estimation methods are derived. We illustrate the effectiveness of the methods through theoretical examples, simulation experiments and by analyzing a data set on yearly total precipitation anomalies at weather stations in the United States.

Original languageEnglish
Pages (from-to)877-892
Number of pages16
JournalStatistics and Computing
Issue number5
StatePublished - 3 Sep 2015


  • Covariance estimation
  • Geostatistics
  • Large datasets
  • Tapering


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