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Gaussian component mixtures and CAR models in Bayesian disease mapping

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Gaussian component mixtures and CAR models in Bayesian disease mapping. / Moraga, P.; Lawson, A. B.
In: Computational Statistics and Data Analysis, Vol. 56, No. 6, 06.2012, p. 1417-1433.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Moraga, P & Lawson, AB 2012, 'Gaussian component mixtures and CAR models in Bayesian disease mapping', Computational Statistics and Data Analysis, vol. 56, no. 6, pp. 1417-1433. https://doi.org/10.1016/j.csda.2011.11.011

APA

Moraga, P., & Lawson, A. B. (2012). Gaussian component mixtures and CAR models in Bayesian disease mapping. Computational Statistics and Data Analysis, 56(6), 1417-1433. https://doi.org/10.1016/j.csda.2011.11.011

Vancouver

Moraga P, Lawson AB. Gaussian component mixtures and CAR models in Bayesian disease mapping. Computational Statistics and Data Analysis. 2012 Jun;56(6):1417-1433. doi: 10.1016/j.csda.2011.11.011

Author

Moraga, P. ; Lawson, A. B. / Gaussian component mixtures and CAR models in Bayesian disease mapping. In: Computational Statistics and Data Analysis. 2012 ; Vol. 56, No. 6. pp. 1417-1433.

Bibtex

@article{f81947bc49b146fba546deb3b5b4ad64,
title = "Gaussian component mixtures and CAR models in Bayesian disease mapping",
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.",
keywords = "Disease mapping, Gaussian component mixture models, Conditional autoregressive models",
author = "P. Moraga and Lawson, {A. B.}",
year = "2012",
month = jun,
doi = "10.1016/j.csda.2011.11.011",
language = "English",
volume = "56",
pages = "1417--1433",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",
number = "6",

}

RIS

TY - JOUR

T1 - Gaussian component mixtures and CAR models in Bayesian disease mapping

AU - Moraga, P.

AU - Lawson, A. B.

PY - 2012/6

Y1 - 2012/6

N2 - 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.

AB - 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.

KW - Disease mapping

KW - Gaussian component mixture models

KW - Conditional autoregressive models

U2 - 10.1016/j.csda.2011.11.011

DO - 10.1016/j.csda.2011.11.011

M3 - Journal article

VL - 56

SP - 1417

EP - 1433

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

IS - 6

ER -