For modeling spatial processes, we introduce a rich class of range anisotropic covariance structures which greatly increases the scope of variogram contours and includes geometric anisotropy and isotropy as special cases. Spatial aspects can also be captured using geographical covariates to create a trend surface. We adopt a Bayesian perspective to study models involving both trend surface and range anisotropic covariance parameters, fitting these models using sampling-based methods. We analyze a data set of scallop catches and withhold ten sites to compare the accuracy and precision of a noiseless version of the predictive distribution for two such parametric models. We also estimate the detrended variogram and, for a particular subregion of interest, predict locally and globally on the original scale
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