  The estimation of parameter standard errors for semi-variogram models is
challenging, given the two-step process required to fit a parametric model to
spatially correlated data. Motivated by an application in the
social-epidemiology, we focus on exponential semi-variogram models fitted to
data between 500 to 2000 observations and little control over the sampling
design. Previously proposed methods for the estimation of standard errors
cannot be applied in this context. Approximate closed form solutions are too
costly using generalized least squares in terms of memory capacities. The
generalized bootstrap proposed by Olea and Pardo-Ig\'uzquiza is nonetheless
applicable with weighted instead of generalized least squares. However, the
standard error estimates are hugely biased and imprecise. Therefore, we propose
a filtering method added to the generalized bootstrap. The new development is
presented and evaluated with a simulation study which shows that the
generalized bootstrap with check-based filtering leads to massively improved
results compared to the quantile-based filter method and previously developed
approaches. We provide a case study using birthweight data.
