Robust and resistant semivariogram modelling using a generalized bootstrap

The bootstrap is a computer-intensive resampling method for estimating
the uncertainty of complex statistical models. We expand on an
application of the bootstrap for inferring semivariogram parameters and
their uncertainty. The model fitted to the median of the bootstrap distribution
of the experimental semivariogram is proposed as an estimator of
the semivariogram. The proposed application is not restricted to normal
data and the estimator is resistant to outliers. Improvements are more
significant for data-sets with less than 100 observations, which are
those for which semivariogram model inference is the most difficult. The
application is illustrated by using it to characterize a synthetic random
field for which the true semivariogram type and parameters are known.