Incorporating parameter uncertainty into prediction intervals for spatial data modeled via a parametric variogram

In spatial predictions, researchers usually treat the estimated theoretical variogram parameters as known without error and ignore the variability of the parameter estimators. Although the prediction is still unbiased, the prediction error is usually underestimated. Therefore, the coverage probability of the prediction interval usually is lower than the nominal probability. A simulation study is performed to show how the coverage probability for prediction relates to the true range and sill of an exponential variogram. This article proposes two parametric bootstrap methods to incorporate the variability of the corresponding parameter estimators. A simulation study is performed to evaluate the coverage probability of these proposed methods. Finally, we apply the parametric bootstrap methods to a real dataset and compare the results with those from naive (i.e., treating estimated parameters as known) and Bayesian methods.