Aline A. Nobre, Bruno Sanso and Alexandra M. Schmidt
01/13/2010 09:00 AM
Applied Mathematics & Statistics
We develop a class of models for processes indexed in time and
space that are based on autoregressive (AR) processes at each location. We use a Bayesian hierarchical structure to impose spatial coherence for the coefficients of the AR processes. The priors on such coefficients consists of spatial process es that guarantee time stationarity at each point in the spatial domain. The AR structures are coupled with a dynamic model for the mean of the process, which i s expressed as a linear combination of time-varying parameters. We use satellite data on sea surface temperature for the North Pacific to illustrate how the mode l can be used to separate trends, cycles and short term variability for high frequency environmental data.
