Statistical Modelling 2 (2002), 315331
Multiresolution models for nonstationary spatial covariance functions
Douglas Nychka,
National Center for Atmospheric Research
P.O. Box 3000,
Boulder, CO 80307-3000
USA
eMail: nychka@ucar.edu
Christopher K. Wikle,
Department of Statistics, University of Missouri,
Columbia, MO
USA
J. Andrew Royle,
US Fish and Wildlife Service Adaptive Management and Assessment
Team,
Laurel, MD,
USA
Abstract:
Many geophysical and environmental problems depend on estimating
a spatial process that has nonstationary structure. A nonstationary
model is proposed based on the spatial field being a linear
combination of multiresolution (wavelet) basis functions and
random coefficients. The key is to allow for a limited number
of correlations among coefficients and also to use a wavelet
basis that is smooth. When approximately 6 % nonzero correlations
are enforced, this representation gives a good approximation to
family of Matern covariance functions. This sparseness is important
not only for model parsimony but also has implications for the
efficient analysis of large spatial data sets. The covariance
model is successfully applied to ozone model output and results
in a nonstationary but smooth estimate.
Keywords:
wavelet; Kriging; multiresolution; ozone pollution
Downloads:
Data and Software available from
http://www.cgd.ucar.edu/~nychka/man.html#wcov
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