Statistics and Computing

, Volume 25, Issue 5, pp 877–892 | Cite as

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

Article

Abstract

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.

Keywords

Covariance estimation Geostatistics Large datasets Tapering 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.DEUVUniversidad de ValparaisoValparaisoChile
  2. 2.DAISUniversità Ca’ Foscari-VeneziaVeneziaItaly

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