Statistical Modelling 2 (2002), 267289
Calibrated Spatial Moving Average Simulations
Noel Cressie,
Director of the Program in Spatial Statistics and Environmental Sciences,
Department of Statistics,
1958 Neil Avenue,
The Ohio State University,
Columbus, Ohio 43210-1247,
USA
eMail:
ncressie@stat.ohio-state.edu
Martina Pavlicová
Department of Statistics,
The Ohio State University,
USA
Abstract:
The spatial moving average (SMA) is a very natural type of spatial
process that involves integrals or sums of independent and
identically distributed random variables. Consequently, the mean and
covariance function of the SMAs can be written down immediately in
terms of their integrand or summand. Moreover, simulation from them is
straightforward, and it does not require any large-matrix
inversions. Although the SMAs generate a large class of spatial covariance
functions, can we find easy-to-use SMAs, calibrated to be "like" some of
the usual
covariance functions used in geostatistics? For example, is there an
SMA that is straightforward to simulate from,
whose covariance function is like the spherical covariance
function? This article will derive such an SMA.
Keywords:
Geostatistics; spatial covariance function; spatial statistics;
spherical model.
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