Dynamic Models for Spatio-Temporal Models
Jonathan R. Stroud, Peter Müller, Bruno Sansó
University of Chicago, MD Anderson Cancer Center, and UC-Santa Cruz
We propose a model for nonstationary spatio-temporal 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.
Kriging-based variogram models can also be incorporated when the process is highly localized in
space. The model is cast in a general state space framework which allows us to include additional
temporal components such as trends, seasonal effects, and autoregressions, and permits fast
implementation and full probabilistic inference for the parameters interpolations and forecasts.
To illustrate our method, we apply it to two large datasets from the physical sciences: quarterly
rainfall levels in Venezuela and water temperatures in the Atlantic Ocean.
The manuscript is available in PDF format.