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Dynamic Models for Spatiotemporal Data
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Titel: |
Dynamic Models for Spatiotemporal Data |
In: | Journal of the Royal Statistical Society Series B: Statistical Methodology, 63, 2001, 4, S. 673-689 |
veröffentlicht: |
Oxford University Press (OUP)
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Umfang: | 673-689 |
ISSN: |
1369-7412 1467-9868 |
DOI: | 10.1111/1467-9868.00305 |
Zusammenfassung: | <jats:title>Summary</jats:title> <jats:p>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.</jats:p> |
Format: | E-Article |
Quelle: | Oxford University Press (OUP) (CrossRef) |
Sprache: | Englisch |