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Spatial modeling using frequentist approach for disease mapping

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  • Mahmoud Torabi

Abstract

In this article, a generalized linear mixed model (GLMM) based on a frequentist approach is employed to examine spatial trend of asthma data. However, the frequentist analysis of GLMM is computationally difficult. On the other hand, the Bayesian analysis of GLMM has been computationally convenient due to the advent of Markov chain Monte Carlo algorithms. Recently developed data cloning (DC) method, which yields to maximum likelihood estimate, provides frequentist approach to complex mixed models and equally computationally convenient method. We use DC to conduct frequentist analysis of spatial models. The advantages of the DC approach are that the answers are independent of the choice of the priors, non-estimable parameters are flagged automatically, and the possibility of improper posterior distributions is completely avoided. We illustrate this approach using a real dataset of asthma visits to hospital in the province of Manitoba, Canada, during 2000--2010. The performance of the DC approach in our application is also studied through a simulation study.

Suggested Citation

  • Mahmoud Torabi, 2012. "Spatial modeling using frequentist approach for disease mapping," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2431-2439, July.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2431-2439
    DOI: 10.1080/02664763.2012.711814
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    References listed on IDEAS

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    1. Mahmoud Torabi & Rhonda J. Rosychuk, 2011. "Spatio-temporal modelling using B-spline for disease mapping: analysis of childhood cancer trends," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1769-1781, October.
    2. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    3. Lele, Subhash R. & Nadeem, Khurram & Schmuland, Byron, 2010. "Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1617-1625.
    4. Hamilton, James D., 1986. "A standard error for the estimated state vector of a state-space model," Journal of Econometrics, Elsevier, vol. 33(3), pages 387-397, December.
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    Cited by:

    1. Mahmoud Torabi, 2014. "Hierarchical Bayesian bivariate disease mapping: analysis of children and adults asthma visits to hospital," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 612-621, March.
    2. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.

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