IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v39y2012i11p2431-2439.html
   My bibliography  Save this article

Spatial modeling using frequentist approach for disease mapping

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2012.711814
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2012.711814?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Craig Anderson & Louise M. Ryan, 2017. "A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia," IJERPH, MDPI, vol. 14(2), pages 1-16, February.
    2. Torabi, Mahmoud, 2013. "Likelihood inference in generalized linear mixed measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 549-557.
    3. Torabi, Mahmoud, 2012. "Likelihood inference in generalized linear mixed models with two components of dispersion using data cloning," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4259-4265.
    4. Torabi, Mahmoud & Lele, Subhash R. & Prasad, Narasimha G.N., 2015. "Likelihood inference for small area estimation using data cloning," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 158-171.
    5. Torabi, Mahmoud & Shokoohi, Farhad, 2012. "Likelihood inference in small area estimation by combining time-series and cross-sectional data," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 213-221.
    6. Victor Bystrov, 2018. "Measuring the Natural Rates of Interest in Germany and Italy," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(4), pages 333-353, December.
    7. Gabriele Fiorentini & Alessandro Galesi & Gabriel Pérez-Quirós & Enrique Sentana, 2018. "The rise and fall of the natural interest rate," Working Papers 1822, Banco de España.
    8. Katherine Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    9. Benati, Luca, 2007. "Drift and breaks in labor productivity," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2847-2877, August.
    10. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    11. Jun Ma & Mark E. Wohar, 2013. "An Unobserved Components Model that Yields Business and Medium-Run Cycles," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1351-1373, October.
    12. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    13. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    14. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    15. Camba-Mendez, Gonzalo, 2012. "Conditional forecasts on SVAR models using the Kalman filter," Economics Letters, Elsevier, vol. 115(3), pages 376-378.
    16. Basistha, Arabinda & Kurov, Alexander, 2010. "Estimating earnings trend using unobserved components framework," Economics Letters, Elsevier, vol. 107(1), pages 55-57, April.
    17. Vanessa Santos-Sánchez & Juan Antonio Córdoba-Doña & Javier García-Pérez & Antonio Escolar-Pujolar & Lucia Pozzi & Rebeca Ramis, 2020. "Cancer Mortality and Deprivation in the Proximity of Polluting Industrial Facilities in an Industrial Region of Spain," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    18. Berti, Patrizia & Dreassi, Emanuela & Rigo, Pietro, 2014. "Compatibility results for conditional distributions," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 190-203.
    19. Louise Choo & Stephen G. Walker, 2008. "A new approach to investigating spatial variations of disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 395-405, April.
    20. Young‐Geun Choi & Lawrence P. Hanrahan & Derek Norton & Ying‐Qi Zhao, 2022. "Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records," Biometrics, The International Biometric Society, vol. 78(1), pages 324-336, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2431-2439. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.