On shortest prediction intervals in log-Gaussian random fields

This work considers the problem of constructing prediction intervals in log-Gaussian random fields. New prediction intervals are derived that are shorter than the standard prediction intervals of common use, where the reductions in length can be substantial in some situations. We consider both the case when the covariance parameters are known and unknown. For the latter case we propose a bootstrap calibration method to obtain prediction intervals with better coverage properties than the plug-in (estimative) prediction intervals. The methodology is illustrated using a spatial dataset consisting of cadmium concentrations from a potentially contaminated region in Switzerland.