Please use this identifier to cite or link to this item: https://repositorio.ufrn.br/jspui/handle/123456789/27480
Title: State space models with spatial deformation
Authors: Morales, Fidel Ernesto Castro
Gamerman, Dani
Paez, Marina Silva
Keywords: Anisotropy;Bayesian inference;Concentrations of sulfur dioxide;MCMC;Minimum temperature;Spatial deformation;State space models
Issue Date: 2013
Publisher: Environmental and Ecological Statistics
Citation: CASTRO, Fidel E. M.; GAMERMAN, Dani ; PAEZ, Marina S. . State space models with spatial deformation. Environmental and Ecological Statistics , v. 20, p. 191-214, 2013. Disponível em:<https://link.springer.com/article/10.1007%2Fs10651-012-0215-2>. Acesso em: 06 dez. 2017
Portuguese Abstract: Space deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal stationarity. In real applications, however, the temporal stationarity assumption is inappropriate and unrealistic. In thisworkwe propose a spatialtemporal model whose temporal trend is modeled through state space models and a spatially varying anisotropy is modeled through spatial deformation, under the Bayesian approach. A distinctive feature of our approach is the consideration of model uncertainty in an unified framework. Our model has a clear advantage over the ones proposed so far in the literature when themain objective of the study is to perform spatial interpolation for fixed points in time. Approximations of the posterior distributions of the model parameters are obtained via Markov chain Monte Carlo methods. This allows for prediction of the process values in space and time as well as handling of missing values. Two applications are presented: the first one to model concentrations of sulfur dioxide in the eastern United States and the second one to model monthly minimum temperatures in the State of Rio de Janeiro.
URI: https://repositorio.ufrn.br/jspui/handle/123456789/27480
Appears in Collections:CCET - DEST - Artigos publicados em periódicos

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