Covariance Tapering for Likelihood Based Estimation in Large Spatial Datasets

@inproceedings{Kaufman2005CovarianceTF,
  title={Covariance Tapering for Likelihood Based Estimation in Large Spatial Datasets},
  author={Cari Kaufman and Mark J. Schervish and Douglas W. Nychka},
  year={2005}
}
Maximum likelihood is an attractive method of estimating covariance parameters in spatial models based on Gaussian processes. However, calculating the likelihood can be computationally infeasible for large datasets, requiring O(n3) calculations for a dataset with n observations. This article proposes the method of covariance tapering to approximate the likelihood in this setting. In this approach, covariance matrices are “tapered,” or multiplied element-wise by a sparse correlation matrix. The… CONTINUE READING
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