In order to make spatial statistics computationally feasible, we need to forget about the covariance function

Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping

(2011) David Bolin et al.   Annals of Applied Statistics

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

(2011) Finn Lindgren et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Fixed rank kriging for very large spatial data sets

(2010) Noel Cressie et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets

(2009) Cari G. Kaufman et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

(2009) Håvard Rue et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Gaussian predictive process models for large spatial data sets

(2008) Sudipto Banerjee et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY