Precision Measures Estimation of Kriging with Spatial Block Bootstrap Method

For spatial data, that are correlated in terms of their locations in the underlined space, the moving block bootstrap method is usually used to estimate the precision measures of the estimators. But in this method, the boundary observations have less chance of presence in blocks resampling than the other observations. In this paper, an algorithm is given for separate block bootstrap to estimate the precision measures of kriging spatial predictor. Then, it is shown that the bias estimation of kriging with separate block bootstrap method is unbiased and the kriging variance estimator is consistent. Finally, in a simulation study, the efficiencies of the separate and moving block bootstrap methods are compared for measures of estimation precision.