Dynamic models for spatiotemporal data

Jonathan R. Stroud, Peter Müller and Bruno Sansó

Journal of the Royal Statistical Society Series B, 2001, vol. 63, issue 4, 673-689

Abstract: We propose a model for non‐stationary spatiotemporal data. To account for spatial variability, we model the mean function at each time period as a locally weighted mixture of linear regressions. To incorporate temporal variation, we allow the regression coefficients to change through time. The model is cast in a Gaussian state space framework, which allows us to include temporal components such as trends, seasonal effects and autoregressions, and permits a fast implementation and full probabilistic inference for the parameters, interpolations and forecasts. To illustrate the model, we apply it to two large environmental data sets: tropical rainfall levels and Atlantic Ocean temperatures.

Date: 2001
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