On identification in Bayesian disease mapping and ecological-spatial regression models.
Stat Methods Med Res
; 23(2): 134-55, 2014 Apr.
Article
em En
| MEDLINE
| ID: mdl-22573502
ABSTRACT
We discuss identification of structural characteristics of the underlying relative risks ensemble for posterior relative risks inference within Bayesian generalized linear mixed model framework for small-area disease mapping and ecological-spatial regression. We revisit conditionally specified and locally characterized Gaussian Markov random field risks ensemble priors in univariate disease mapping and communicate insight into Gaussian Markov random field variance-covariance characteristics for representing disease risks variability and spatial risks interactions and for structural identification with respect to risks ensemble prior choices. Illustrative examples of identification in Bayesian disease mapping and ecological-spatial regression models are presented for Bayesian hierarchical generalized linear mixed Poisson models and zero-inflated Poisson models.
Palavras-chave
Bayesian disease mapping; Gaussian Markov random fields; Leroux et al. conditional autoregressive; generalized linear mixed model; identifiability; identification; intrinsic conditional autoregressive; proper conditional autoregressive; smoothing; spatial interaction; spatial regression; weighted convolution prior; zero-inflated Poisson model
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1
Base de dados:
MEDLINE
Assunto principal:
Modelos Lineares
/
Modelos Estatísticos
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Teorema de Bayes
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article