, stationary covariance tapering and the full-scale approximation are able to notably speed up calculations. However the loss in …-scale approximation, which was able to overcome the individual weaknesses of the fixed rank kriging and the covariance tapering. …

efficient model approximations, covariance tapering and the process convolution method. The methods are compared with respect to …

predictor is impractically time-consuming. To reduce the computational complexity, covariance tapering has been developed for … large spatial data sets. In this paper, we consider covariance tapering in a class of transformed Gaussian models for random … fields and show that the BLUP using covariance tapering, the BLUP and the optimal predictor are asymptotically equivalent in …