A Bayesian Bivariate Model for Spatially Correlated Binary Outcomes
Autor: | Thierno Souleymane Barry, Oscar Ngesa, Nelson Owuor Onyango, Henry Mwambi |
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Rok vydání: | 2022 |
Předmět: | |
Zdroj: | Mathematical Problems in Engineering. 2022:1-8 |
ISSN: | 1563-5147 1024-123X |
DOI: | 10.1155/2022/7852042 |
Popis: | Diseases have been studied separately, but two diseases have inherent dependencies on each other, modelling them separately negates practical reality. The authors’ modelling processes are based on univariate separate regressions, which connect each illness to covariates separately. Therefore, the focus of this article is to estimate the spatial correlation within geographic regions using latent variables. Individual and areal-level information, as well as spatially dependent random effects for each spatial unit, are incorporated into the models developed using a hierarchical structure. Simulation techniques provide to assess the models’ performance using Bayesian computing approaches (INLA and MCMC). The findings show a reasonable performance of the DIC and RMSE values of the proposed latent model. From that, the model can be considered as the best compared to the shared component model, multivariate conditional autoregressive model, and univariate models. |
Databáze: | OpenAIRE |
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