Covariance Tapering for Likelihood Based Estimation in Large Spatial Datasets

  title={Covariance Tapering for Likelihood Based Estimation in Large Spatial Datasets},
  author={Cari Kaufman and Mark J. Schervish and Douglas W. Nychka},
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
Highly Influential
This paper has highly influenced 12 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 308 citations. REVIEW CITATIONS


Publications citing this paper.

309 Citations

Citations per Year
Semantic Scholar estimates that this publication has 309 citations based on the available data.

See our FAQ for additional information.