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Spatially Varying Autoregressive Processes

Summary: [This abstract is based on the authors' abstract.] A class of models is developed for time- and space-indexed processes based on autoregressive (AR) processes at each location. A Bayesian hierarchical structure imposes spatial coherence for the AR process coefficients. These AR structures are combined with a dynamic model for the process mean, expressed as a linear combination of parameters varying by time. An example using sea surface temperature data shows how the model can separate trends, cycles, short-term variability, and high-frequency environmental data.

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  • Topics: Statistics
  • Keywords: Autoregression, Bayesian methods, Space-time modeling
  • Author: Nobre, Aline A.; Sansó, Bruno; Schmidt, Alexandra M.;
  • Journal: Technometrics