Abstract: Within geostatistics research, effective estimation of
the variogram points has been examined, particularly in developing
robust alternatives. The parametric fit of these variogram points which
eventually defines the kriging weights, however, has not received
the same attention from a robust perspective. This paper proposes
the use of the non-linear Wilcoxon norm over weighted non-linear
least squares as a robust variogram fitting alternative. First, we
introduce the concept of variogram estimation and fitting. Then, as
an alternative to non-linear weighted least squares, we discuss the
non-linear Wilcoxon estimator. Next, the robustness properties of the
non-linear Wilcoxon are demonstrated using a contaminated spatial
data set. Finally, under simulated conditions, increasing levels of
contaminated spatial processes have their variograms points estimated
and fit. In the fitting of these variogram points, both non-linear
Weighted Least Squares and non-linear Wilcoxon fits are examined
for efficiency. At all levels of contamination (including 0%), using
a robust estimation and robust fitting procedure, the non-weighted
Wilcoxon outperforms weighted Least Squares.