Estimating space and space-time covariance functions for large data sets: A weighted composite likelihood approach

Moreno Bevilacqua, Carlo Gaetan, Jorge Mateu, Emilio Porcu

Research output: Contribution to journalArticlepeer-review

92 Scopus citations

Abstract

In this article, we propose two methods for estimating space and space-time covariance functions from a Gaussian random field, based on the composite likelihood idea. The first method relies on the maximization of a weighted version of the composite likelihood function, while the second one is based on the solution of a weighted composite score equation. This last scheme is quite general and could be applied to any kind of composite likelihood. An information criterion for model selection based on the first estimation method is also introduced. The methods are useful for practitioners looking for a good balance between computational complexity and statistical efficiency. The effectiveness of the methods is illustrated through examples, simulation experiments, and by analyzing a dataset on ozone measurements.

Original languageEnglish
Pages (from-to)268-280
Number of pages13
JournalJournal of the American Statistical Association
Volume107
Issue number497
DOIs
StatePublished - 2012

Keywords

  • Composite likelihood
  • Estimating equations
  • Godambe information
  • Identification
  • Space-time geostatistics

Fingerprint

Dive into the research topics of 'Estimating space and space-time covariance functions for large data sets: A weighted composite likelihood approach'. Together they form a unique fingerprint.

Cite this