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• W2076487641 abstract "Summary 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." @default.
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• W2076487641 date "2001-11-01" @default.
• W2076487641 modified "2023-10-17" @default.
• W2076487641 title "Dynamic Models for Spatiotemporal Data" @default.
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• W2076487641 doi "https://doi.org/10.1111/1467-9868.00305" @default.
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