Neighborhood dependence in Bayesian spatial models.
Biom J
; 51(5): 851-69, 2009 Oct.
Article
em En
| MEDLINE
| ID: mdl-19827056
ABSTRACT
The conditional autoregressive model and the intrinsic autoregressive model are widely used as prior distribution for random spatial effects in Bayesian models. Several authors have pointed out impractical or counterintuitive consequences on the prior covariance matrix or the posterior covariance matrix of the spatial random effects. This article clarifies many of these puzzling results. We show that the neighborhood graph structure, synthesized in eigenvalues and eigenvectors structure of a matrix associated with the adjacency matrix, determines most of the apparently anomalous behavior. We illustrate our conclusions with regular and irregular lattices including lines, grids, and lattices based on real maps.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
1_ASSA2030
Problema de saúde:
1_financiamento_saude
Assunto principal:
Teorema de Bayes
/
Biometria
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Biom J
Ano de publicação:
2009
Tipo de documento:
Article
País de afiliação:
Brasil