Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
Abstract Maximum likelihood is an attractive method of estimating covariance parameters in spatial models based on Gaussian processes. But calculating the likelihood can be computationally infeasible for large data sets, requiring O(n3) calculations for a data set with n observations. This article proposes the method of covariance tapering to approximate …