• Corpus ID: 53654185

Covariance tapering for anisotropic nonsta-tionary Gaussian random fields with appli-cation to large scale spatial data sets

  title={Covariance tapering for anisotropic nonsta-tionary Gaussian random fields with appli-cation to large scale spatial data sets},
  author={Abolfazl Safikhani and Yimin Xiao},
Estimating the covariance structure of spatial random processes is an important step in spatial data analysis. Maximum likelihood estimation is a popular method in spatial models based on Gaussian random fields. But calculating the likelihood in large scale data sets is computationally infeasible due to the heavy computation of the precision matrix. One way to mitigate this issue, which is due to Furrer et al. (2006), is to “taper” the covariance matrix. While most of the results in the current… 

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