Bootstrap based Trans-Gaussian Kriging

Trans-Gaussian Kriging is a popular method for predicting unobserved values of a nonstationary and non-Gaussian spatial process. However, the predictions generated by Trans-Gaussian Kriging are often biased, primarily because these are produced by applying nonlinear transformations to unbiased optimal predictors of the underlying Gaussian field. The existing approach to bias correction is based on analytical approximations that only alleviate the bias problem partially. In this paper, we formulate a bootstrap method for bias correction and show that under some conditions, it yields asymptotically unbiased predictions. Results from a moderately large simulation show that the proposed method works well in reducing the bias in finite samples. A real data example is also presented illustrating the methodology.