Abstract Machine learning and geostatistics are powerful mathematical frameworks for modeling spatial data. Both approaches, however, suffer from poor scaling of the required computational resources for large data applications. We present the Stochastic Local Interaction (SLI) model, which employs a local representation to improve computational efficiency. SLI combines geostatistics and machine...

In geostatistics, one of the crucial problems is the choice of the covariance function. In this paper we show how to improve the cross-validation criterion, traditionally used for evaluating the /t of a covariance function, in the case of unequally spaced data. c © 2001 Elsevier Science B.V. All rights reserved

Interpolating scattered data points is a problem of wide ranging interest. One of the most popular interpolation methods in geostatistics is ordinary kriging. The price for its statistical optimality is that the estimator is computationally very expensive. We demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined ...