IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v76y2020i2p530-539.html
   My bibliography  Save this article

Bias correction of bounded location error in binary data

Author

Listed:
  • Nelson B. Walker
  • Trevor J. Hefley
  • Daniel P. Walsh

Abstract

Binary regression models for spatial data are commonly used in disciplines such as epidemiology and ecology. Many spatially referenced binary data sets suffer from location error, which occurs when the recorded location of an observation differs from its true location. When location error occurs, values of the covariates associated with the true spatial locations of the observations cannot be obtained. We show how a change of support (COS) can be applied to regression models for binary data to provide coefficient estimates when the true values of the covariates are unavailable, but the unknown location of the observations are contained within nonoverlapping arbitrarily shaped polygons. The COS accommodates spatial and nonspatial covariates and preserves the convenient interpretation of methods such as logistic and probit regression. Using a simulation experiment, we compare binary regression models with a COS to naive approaches that ignore location error. We illustrate the flexibility of the COS by modeling individual‐level disease risk in a population using a binary data set where the locations of the observations are unknown but contained within administrative units. Our simulation experiment and data illustration corroborate that conventional regression models for binary data that ignore location error are unreliable, but that the COS can be used to eliminate bias while preserving model choice.

Suggested Citation

  • Nelson B. Walker & Trevor J. Hefley & Daniel P. Walsh, 2020. "Bias correction of bounded location error in binary data," Biometrics, The International Biometric Society, vol. 76(2), pages 530-539, June.
  • Handle: RePEc:bla:biomet:v:76:y:2020:i:2:p:530-539
    DOI: 10.1111/biom.13152
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13152
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.13152?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
    ---><---

    References listed on IDEAS

    as
    1. Peter J. Diggle & Barry S. Rowlingson, 1994. "A Conditional Approach to Point Process Modelling of Elevated Risk," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 433-440, May.
    2. 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.
    3. Jonathan R. Bradley & Christopher K. Wikle & Scott H. Holan, 2016. "Bayesian Spatial Change of Support for Count-Valued Survey Data With Application to the American Community Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 472-487, April.
    Full references (including those not matched with items on IDEAS)

    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. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    2. Groß Marcus & Kreutzmann Ann-Kristin & Rendtel Ulrich & Schmid Timo & Tzavidis Nikos, 2020. "Switching Between Different Non-Hierachical Administrative Areas via Simulated Geo-Coordinates: A Case Study for Student Residents in Berlin," Journal of Official Statistics, Sciendo, vol. 36(2), pages 297-314, June.
    3. Leo Polansky & Ken B. Newman & Lara Mitchell, 2021. "Improving inference for nonlinear state‐space models of animal population dynamics given biased sequential life stage data," Biometrics, The International Biometric Society, vol. 77(1), pages 352-361, March.
    4. Bermudez, P. de Zea & Marín, J. Miguel & Rue, Håvard & Veiga, Helena, 2024. "Integrated nested Laplace approximations for threshold stochastic volatility models," Econometrics and Statistics, Elsevier, vol. 30(C), pages 15-35.
    5. 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.
    6. Lawson, Andrew B. & Simeon, Silvia & Kulldorff, Martin & Biggeri, Annibale & Magnani, Corrado, 2007. "Line and point cluster models for spatial health data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6027-6043, August.
    7. Alonso, Pablo J., 2015. "Hierarchical Lee-Carter model estimation through data cloning applied to demographically linked countries," DES - Working Papers. Statistics and Econometrics. WS ws1510, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Minxian Yang, 2014. "Normality of Posterior Distribution Under Misspecification and Nonsmoothness, and Bayes Factor for Davies' Problem," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 305-336, June.
    9. Marco Gramatica & Peter Congdon & Silvia Liverani, 2021. "Bayesian modelling for spatially misaligned health areal data: A multiple membership approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 645-666, June.
    10. Campbell, David & Lele, Subhash, 2014. "An ANOVA test for parameter estimability using data cloning with application to statistical inference for dynamic systems," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 257-267.
    11. Romero, Eva, 2024. "A stochastic volatility model for volatility asymmetry and propagation," DES - Working Papers. Statistics and Econometrics. WS 43887, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. P. de Zea Bermudez & J. Miguel Marín & Helena Veiga, 2020. "Data cloning estimation for asymmetric stochastic volatility models," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1057-1074, November.
    13. Gul Inan & Ozlem Ilk, 2019. "A marginalized multilevel model for bivariate longitudinal binary data," Statistical Papers, Springer, vol. 60(3), pages 601-628, June.
    14. Laurini Márcio Poletti, 2013. "A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 193-229, May.
    15. Torabi, Mahmoud, 2013. "Likelihood inference in generalized linear mixed measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 549-557.
    16. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    17. S P Kingham & A C Gatrell & B Rowlingson, 1995. "Testing for Clustering of Health Events within a Geographical Information System Framework," Environment and Planning A, , vol. 27(5), pages 809-821, May.
    18. Savitsky Terrance D. & Williams Matthew R., 2022. "Pseudo Bayesian Mixed Models under Informative Sampling," Journal of Official Statistics, Sciendo, vol. 38(3), pages 901-928, September.
    19. Davidson, Marty, 2024. "Strategic Point Processes," OSF Preprints g5r9t, Center for Open Science.
    20. Kerstin Erfurth & Marcus Groß & Ulrich Rendtel & Timo Schmid, 2022. "Kernel density smoothing of composite spatial data on administrative area level [Die Glättung räumlicher Datensätze auf administrativen Flächen]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(1), pages 25-49, 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:76:y:2020:i:2:p:530-539. 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.