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Publicação
Disease mapping models for data with weak spatial dependence or spatial discontinuities
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| 3.4 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Recent advances in the spatial epidemiology literature have extended traditional approaches by
including determinant disease factors that allow for non-local smoothing and/or non-spatial smoothing. In
this article, two of those approaches are compared and are further extended to areas of high interest from the
public health perspective. These are a conditionally specified Gaussian random field model, using a similaritybased non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a
spatially adaptive conditional autoregressive prior model.
Descrição
Palavras-chave
Bayesian modelling Bayesian modelling Limiting health problems Spatial epidemiology Similarity-based and adaptive models