A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

作者：Castruccio Stefano^*; Bonaventura Luca; Sangalli Laura M

来源：Journal of Agricultural, Biological, and Environmental Statistics, 2012, 17(2): 209-227.

DOI：10.1007/s13253-012-0086-x

• Markov chain Monte Carlo
• Kriging
• Variogram estimation

A Bayesian approach to covariance estimation and spatial prediction based on flexible variogram models is introduced. In particular, we consider black-box kriging models. These variogram models do not require restrictive assumptions on the functional shape of the variogram; furthermore, they can handle quite naturally non isotropic random fields. The proposed Bayesian approach does not require the computation of an empirical variogram estimator, thus avoiding the arbitrariness implied in the construction of the empirical variogram itself. Moreover, it provides a complete assessment of the uncertainty in the variogram estimation. The advantages of this approach are illustrated via simulation studies and by application to a well known benchmark dataset.

• 出版日期2012-6