|
Abstract
|
In this article, we consider Hilbertian spatial periodically correlated autoregressive models. Such a spatial model assumes periodicity in its autocorrelation function. Plausibly, it explains spatial functional data resulted from phenomena with periodic structures, as geological, atmospheric, meteorological and oceanographic data.
Our studies on these models include model building, existence, time domain moving
average representation, least square parameter estimation and prediction based on the
autoregressive structured past data. We also fit a model of this type to a real data of
invisible infrared satellite images.
|