Statistical Modelling 2 (2002), 299–314
A kernel-based spectral model for non-Gaussian spatio-temporal processes
Christopher K. Wikle,
Department of Statistics, University of Missouri,
222 Math Science Building,
Columbia, MO 65211
USA
eMail: wikle@stat.missouri.edu
Abstract:
Spatio-Temporal processes can often be written as hierarchical
state-space processes. In situations with complicated dynamics
such as wave propagation, it is difficult to parameterize state
transition functions for high-dimensional state processes.
Although in some case prior understanding of the physical process
can be used to formulate models for the state transition, this is
not always possible. Alternatively, for processes where one
considers discrete time and continuous space, complicated dynamics
can be modeled by stochastic integro-difference equations in which
the associated redistribution kernel is allowed to vary with space
and/or time. By considering a spectral implementation of such models,
one can formulate a spatio-temporal model with relatively few
parameters that can accommodate complicated dynamics. This
approach can be developed in a hierarchical framework for
non-Gaussian processes, as demonstrated on cloud intensity data.
Keywords:
Bayesian; dilation; dynamic models; hierarchical;
integro-difference equations; translation
back