Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/5043
Title: Bayesian spatial models with a mixture neighborhood structure.
Authors: Rodrigues, Erica Castilho
Assunção, Renato Martins
Keywords: Disease mapping
Markov random field
Spatial hierarchical models
Issue Date: 2012
Citation: RODRIGUES, 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.
Abstract: In 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.
URI: http://www.repositorio.ufop.br/handle/123456789/5043
metadata.dc.identifier.doi: https://doi.org/10.1016/j.jmva.2012.02.017
ISSN: 0047-259X
metadata.dc.rights.license: O periódico Journal of Multivariate Analysis concede permissão para depósito do artigo no Repositório Institucional da UFOP. Número da licença: 3603161455883.
Appears in Collections:DEEST - Artigos publicados em periódicos

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