IDEAS home Printed from https://ideas.repec.org/a/bes/jnlasa/v97y2002mseptemberp692-701.html
   My bibliography  Save this article

Modeling Spatial Variation in Disease Risk: A Geostatistical Approach

Author

Listed:
  • Kelsall J.
  • Eld J.W.

Abstract

No abstract is available for this item.

Suggested Citation

  • Kelsall J. & Eld J.W., 2002. "Modeling Spatial Variation in Disease Risk: A Geostatistical Approach," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 692-701, September.
    • Handle: RePEc:bes:jnlasa:v:97:y:2002:m:september:p:692-701
    as

    Download full text from publisher

    File URL: http://www.ingentaconnect.com/content/asa/jasa/2002/00000097/00000459/art00004
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Mahdi Hosseinpouri & Majid Jafari Khaledi, 2019. "An area-specific stick breaking process for spatial data," Statistical Papers, Springer, vol. 60(1), pages 199-221, February.
    2. Mohammadreza Mohebbi & Rory Wolfe & Andrew Forbes, 2014. "Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach," IJERPH, MDPI, vol. 11(1), pages 1-20, January.
    3. Govind, Rahul & Chatterjee, Rabikar & Mittal, Vikas, 2008. "Timely access to health care: Customer-focused resource allocation in a hospital network," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 294-300.
    4. Zhang, Tonglin & Lin, Ge, 2016. "On Moran’s I coefficient under heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 83-94.
    5. Lee, Dae-Jin & Durbán, María, 2009. "Smooth-CAR mixed models for spatial count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2968-2979, June.
    6. Hong-Ding Yang & Yun-Huan Lee & Che-Yang Lin, 2023. "On Study of the Occurrence of Earth-Size Planets in Kepler Mission Using Spatial Poisson Model," Mathematics, MDPI, vol. 11(11), pages 1-14, May.
    7. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2015. "Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i07).
    8. 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.
    9. Lee, Dae-Jin & Durbán, María, 2008. "Smooth-car mixed models for spatial count data," DES - Working Papers. Statistics and Econometrics. WS ws085820, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Ayma Anza, Diego Armando & Durbán, María & Lee, Dae-Jin & Eilers, Paul, 2015. "Penalized composite link mixed models for two-dimensional count data," DES - Working Papers. Statistics and Econometrics. WS ws1509, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Jun Wang & Yang Wang & Hui Zeng, 2016. "A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis," Journal of Geographical Systems, Springer, vol. 18(1), pages 45-66, January.
    12. Laia Maynou & Marc Saez & Jordi Bacaria & Guillem Lopez-Casasnovas, 2015. "Health inequalities in the European Union: an empirical analysis of the dynamics of regional differences," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(5), pages 543-559, June.
    13. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    14. Dolores Catelan & Annibale Biggeri & Corrado Lagazio, 2009. "On the clustering term in ecological analysis: how do different prior specifications affect results?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 49-61, March.
    15. Zhang Hongmei & Gan Jianjun, 2012. "A Reproducing Kernel-Based Spatial Model in Poisson Regressions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-26, October.

    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:bes:jnlasa:v:97:y:2002:m:september:p:692-701. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .

    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.