Spatially Varying Autoregressive Processes

dc.creatorNobre, Aline Araujo
dc.creatorSanso, Bruno
dc.creatorSchmidt, Alexandra Mello
dc.date2019-09-12T17:20:16Z
dc.date2019-09-12T17:20:16Z
dc.date2011
dc.date.accessioned2023-09-26T20:52:50Z
dc.date.available2023-09-26T20:52:50Z
dc.identifierNOBRE, Aline Araujo; SANSO, Bruno; SCHMIDT, Alexandra Mello. Spatially Varying Autoregressive Processes. Technometrics, v. 53, n. 3, p. 310-321, Aug. 2011.
dc.identifier0040-1706
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/35550
dc.identifier10.1198/TECH.2011.10008
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8865501
dc.descriptionWe develop a class of models for processes indexed in time and space that are based on autoregressive (AR) processes at each location. We use a Bayesian hierarchical structure to impose spatial coherence for the coefficients of the AR processes. The priors on such coefficients consist of spatial processes that guarantee time stationarity at each point in the spatial domain. The AR structures are coupled with a dynamic model for the mean of the process, which is expressed as a linear combination of time-varying parameters. We use satellite data on sea surface temperature for the North Pacific to illustrate how the model can be used to separate trends, cycles, and short-term variability for high-frequency environmental data. This article has supplementary material online.
dc.formatapplication/pdf
dc.rightsrestricted access
dc.subjectTime series models
dc.subjectAutoregressive models
dc.subjectSpatial models
dc.subjectTime series
dc.subjectSpacetime
dc.subjectCovariance
dc.subjectMultilevel models
dc.subjectInference
dc.subjectPredictive modeling
dc.subjectEcological modeling
dc.titleSpatially Varying Autoregressive Processes
dc.typeArticle


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