Preprint #96-34
Inference for Spatial Processes Using Subsampling: A
Simulation Study
by
Mark S. Kaiser, Nan-Jung Hsu, Noel Cressie and Soumendra N. Lahiri
Abstract
Many environmental studies involve the measurement of ecological indices
that yield spatially dependent data. One quantity that captures the
empirical distribution of ecological measurements is the spatial cumulative
distribution function (SCDF). Methods for making inferential statements
about SCDFs have only recently been developed, one being that of spatial
subsampling. While spatial subsampling produces inferential quantities
with known asymptotic properties, the performance of this methodology
in a finite-sample setting has not previously been investigated. In this
article, we review the subsampling method and its theoretical justification,
and investigate the performance of this method for finite samples with a
simulation study involving several subsampling designs and types of spatial
dependence. The subsampling methodology appears to give quite good results
over a range of realistic spatial processes. For application to a set of
spatially dependent data, an appropriate subsampling procedure may be
designed on the basis of quantities contained in the (estimated) variogram.
Copies of preprints are available from the author upon request. Use
the preprint number (located at the top of the page) and
make the request directly to the author, Iowa State
University,
Department of Statistics, Snedecor Hall, Ames, IA 50011-1210.