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Research Report SRR95-043
On bootstrap methods for spatial data
Pilar H. Garcia-Soidan and Peter Hall
Abstract:
We describe a resampling method for constructing
distribution estimators, and hence for calculating confidence intervals,
in the context of statistics computed from non-replicated spatial data.
Our method is related to the spatial block bootstrap, but differs in
that the full spatial pattern is not actually simulated. Instead, a
resampling algorithm is employed to, as a first step, compute distribution
estimators in the special case of data from a subset of
the observation region. In the second step these estimators are recalibrated,
using a device based on mixtures of distributions, to produce distribution
estimators for statistics computed from the full data set. An empirical
method is suggested for selecting the appropriate subset of the
observation region for the first step of the
algorithm. Numerical applications of the technique are illustrated.
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