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Bayesian spatial models with a mixture neighborhood structure

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  • Rodrigues, E.C.
  • Assunção, R.

Abstract

In Bayesian disease mapping, one needs to specify a neighborhood structure to make inference about the underlying geographical relative risks. We propose a model in which the neighborhood structure is part of the parameter space. We retain the Markov property of the typical Bayesian spatial models: given the neighborhood graph, disease rates follow a conditional autoregressive model. However, the neighborhood graph itself is a parameter that also needs to be estimated. We investigate the theoretical properties of our model. In particular, we investigate carefully the prior and posterior covariance matrix induced by this random neighborhood structure, providing interpretation for each element of these matrices.

Suggested Citation

  • Rodrigues, E.C. & Assunção, R., 2012. "Bayesian spatial models with a mixture neighborhood structure," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 88-102.
    • Handle: RePEc:eee:jmvana:v:109:y:2012:i:c:p:88-102
    DOI: 10.1016/j.jmva.2012.02.017
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    References listed on IDEAS

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    Cited by:

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    2. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2020. "Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia," Journal of Geographical Systems, Springer, vol. 22(1), pages 105-142, January.
    3. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    4. Osafu Augustine Egbon & Omodolapo Somo-Aina & Ezra Gayawan, 2021. "Spatial Weighted Analysis of Malnutrition Among Children in Nigeria: A Bayesian Approach," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 495-523, December.
    5. Gehong Zhang & Junming Li & Sijin Li & Yang Wang, 2018. "Exploring Spatial Trends and Influencing Factors for Gastric Cancer Based on Bayesian Statistics: A Case Study of Shanxi, China," IJERPH, MDPI, vol. 15(9), pages 1-17, August.

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