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Gaussian component mixtures and CAR models in Bayesian disease mapping

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>06/2012
<mark>Journal</mark>Computational Statistics and Data Analysis
Issue number6
Number of pages17
Pages (from-to)1417-1433
Publication StatusPublished
<mark>Original language</mark>English


Hierarchical Bayesian models involving conditional autoregression (CAR) components are commonly used in disease mapping. An alternative model to the proper or improper CAR is the Gaussian component mixture (GCM) model. A review of CAR and GCM models is provided in univariate settings where only one disease is considered, and also in multivariate situations where in addition to the spatial dependence between regions, the dependence among multiple diseases is analyzed. A performance comparison between models using a set of simulated data to help illustrate their respective properties is reported. The results show that both in univariate and multivariate settings, both models perform in a comparable way under a wide range of conditions. GCM and CAR models are applied for estimating the relative risk of low birth weight in Georgia, USA, in the year 2000.