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Dynamic models for spatiotemporal data
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DOI：10.1111/1467-9868.00305.png)
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Abstract
En 中文
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.
Keywords:
Bayesian inference
locally weighted mixture
on-line inference
space-time modelling
state space models
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