IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v59y2003i1p9-17.html
   My bibliography  Save this article

Sensitivity Analyses for Ecological Regression

Author

Listed:
  • Jon Wakefield

Abstract

No abstract is available for this item.

Suggested Citation

  • Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:1:p:9-17
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/1541-0420.00002
    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.

    References listed on IDEAS

    as
    1. 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.
    2. Katherine A. Guthrie & Lianne Sheppard & Jon Wakefield, 2002. "A Hierarchical Aggregate Data Model with Spatially Correlated Disease Rates," Biometrics, The International Biometric Society, vol. 58(4), pages 898-905, December.
    3. Jonathan Wakefield & Ruth Salway, 2001. "A statistical framework for ecological and aggregate studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 119-137.
    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. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
    2. Nuoo‐Ting Molitor & Nicky Best & Chris Jackson & Sylvia Richardson, 2009. "Using Bayesian graphical models to model biases in observational studies and to combine multiple sources of data: application to low birth weight and water disinfection by‐products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 615-637, June.
    3. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    4. Cici Bauer & Jon Wakefield, 2018. "Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1379-1398, November.
    5. Andrea J. Cook & Yi Li & David Arterburn & Ram C. Tiwari, 2010. "Spatial Cluster Detection for Weighted Outcomes Using Cumulative Geographic Residuals," Biometrics, The International Biometric Society, vol. 66(3), pages 783-792, September.
    6. Brian Gray & Vyacheslav Lyubchich & Yulia Gel & James Rogala & Dale Robertson & Xiaoqiao Wei, 2016. "Estimation of river and stream temperature trends under haphazard sampling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 89-105, March.
    7. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    8. Sebastien J.‐P. A. Haneuse & And Jonathan C. Wakefield, 2008. "The combination of ecological and case–control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 73-93, February.
    9. Sebastien J-P. A. Haneuse & Jonathan C. Wakefield, 2007. "Hierarchical Models for Combining Ecological and Case–Control Data," Biometrics, The International Biometric Society, vol. 63(1), pages 128-136, March.
    10. Daniel A. Griffith & Richard E. Plant, 2022. "Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects," Stats, MDPI, vol. 5(4), pages 1-20, December.
    11. Christopher Jackson & And Nicky Best & Sylvia Richardson, 2008. "Hierarchical related regression for combining aggregate and individual data in studies of socio‐economic disease risk factors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 159-178, January.
    12. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach," Environment and Planning A, , vol. 39(5), pages 1193-1221, May.
    13. Angelsen, Arild & Jagger, Pamela & Babigumira, Ronnie & Belcher, Brian & Hogarth, Nicholas J. & Bauch, Simone & Börner, Jan & Smith-Hall, Carsten & Wunder, Sven, 2014. "Environmental Income and Rural Livelihoods: A Global-Comparative Analysis," World Development, Elsevier, vol. 64(S1), pages 12-28.
    14. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, 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. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, July.
    2. Francesca Dominici & Lianne Sheppard & Merlise Clyde, 2003. "Health Effects of Air Pollution: A Statistical Review," International Statistical Review, International Statistical Institute, vol. 71(2), pages 243-276, August.
    3. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
    4. Katherine A. Guthrie & Lianne Sheppard & Jon Wakefield, 2002. "A Hierarchical Aggregate Data Model with Spatially Correlated Disease Rates," Biometrics, The International Biometric Society, vol. 58(4), pages 898-905, December.
    5. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    6. Duncan Lee & Chris Robertson & Colin Ramsay & Kate Pyper, 2020. "Quantifying the impact of the modifiable areal unit problem when estimating the health effects of air pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    7. Katie 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Berti, Patrizia & Dreassi, Emanuela & Rigo, Pietro, 2014. "Compatibility results for conditional distributions," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 190-203.
    14. 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.
    15. 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.
    16. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    17. Eric C. Tassone & Marie Lynn Miranda & Alan E. Gelfand, 2010. "Disaggregated spatial modelling for areal unit categorical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 175-190, January.
    18. Junming Li & Xiulan Han & Xiao Li & Jianping Yang & Xuejiao Li, 2018. "Spatiotemporal Patterns of Ground Monitored PM 2.5 Concentrations in China in Recent Years," IJERPH, MDPI, vol. 15(1), pages 1-15, January.
    19. Sanjay Chaudhuri & Debashis Mondal & Teng Yin, 2017. "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 293-320, January.
    20. 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.

    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:bla:biomet:v:59:y:2003:i:1:p:9-17. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.