Statistical Modelling 2 (2002), 139161
Geir Storvik
University of Oslo and the Norwegian Computing Center,
Norway
e-mail: geirs@math.uio.no
Arnoldo Frigessi
Norwegian Computing Center,
Norway
David Hirst
Norwegian Computing Center,
Norway
The second strategy is to model the time-evolution of the process more directly. We consider models of the autoregressive type where the process at time t is obtained by convolving the process at time t-1 and adding spatially correlated noise.
Under specific conditions, the two strategies describe two different formulations of the same stochastic process. We show how the two representations look in different cases. Furthermore, by transforming time-dynamic convolution models to Gaussian fields we can obtain new covariance functions and by writing a Gaussian field as a time-dynamic convolution model, interesting properties are discovered
The computational aspects of the two strategies are discussed through experiments on a dataset of daily UK temperatures. Although algorithms for performing estimation, simulation and so on are easy to do for the first strategy, more computer efficient algorithms based on the second strategy can be constructed.