Gaussian component mixtures and CAR models in Bayesian disease mapping

Moraga, P. and Lawson, A. B. (2012) Gaussian component mixtures and CAR models in Bayesian disease mapping. Computational Statistics and Data Analysis, 56 (6). pp. 1417-1433. ISSN 0167-9473

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Computational Statistics and Data Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
125750
Deposited By:
Deposited On:
07 Jun 2018 07:54
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2020 08:05