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dc.contributor.authorRodrigues, Erica Castilho-
dc.contributor.authorAssunção, Renato Martins-
dc.date.accessioned2015-04-13T18:55:55Z-
dc.date.available2015-04-13T18:55:55Z-
dc.date.issued2012-
dc.identifier.citationRODRIGUES, E. C.; ASSUNÇÃO, R. M. Bayesian spatial models with a mixture neighborhood structure. Journal of Multivariate Analysis, v. 109, p. 88-102, 2012. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0047259X12000589>. Acesso em: 13 abr. 2015.pt_BR
dc.identifier.issn0047-259X-
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/5043-
dc.description.abstractIn Bayesian disease mapping, one needs to specify a neighborhood structure to make inference about the underlying geographical relative risks. We propose a model in which the neighborhood structure is part of the parameter space. We retain the Markov property of the typical Bayesian spatial models: given the neighborhood graph, disease rates follow a conditional autoregressive model. However, the neighborhood graph itself is a parameter that also needs to be estimated. We investigate the theoretical properties of our model. In particular, we investigate carefully the prior and posterior covariance matrix induced by this random neighborhood structure, providing interpretation for each element of these matrices.pt_BR
dc.language.isoen_USpt_BR
dc.subjectDisease mappingpt_BR
dc.subjectMarkov random fieldpt_BR
dc.subjectSpatial hierarchical modelspt_BR
dc.titleBayesian spatial models with a mixture neighborhood structure.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.rights.licenseO periódico Journal of Multivariate Analysis concede permissão para depósito do artigo no Repositório Institucional da UFOP. Número da licença: 3603161455883.pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.jmva.2012.02.017-
Appears in Collections:DEEST - Artigos publicados em periódicos

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