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The regression-calibration method for fitting generalized linear models with additive measurement error

Author

Listed:
  • James W. Hardin

    (Arnold School of Public Health, University of South Carolina)

  • Henrik Schmeidiche

    (Department of Statistics, Texas A&M University)

  • Raymond J. Carroll

    (Department of Statistics, Texas A&M University)

Abstract

This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on software developed as part of a small business innovation research (SBIR) grant from the National Institutes of Health (NIH). Copyright 2003 by StataCorp LP.

Suggested Citation

  • James W. Hardin & Henrik Schmeidiche & Raymond J. Carroll, 2003. "The regression-calibration method for fitting generalized linear models with additive measurement error," Stata Journal, StataCorp LP, vol. 3(4), pages 361-372, December.
    • Handle: RePEc:tsj:stataj:v:3:y:2003:i:4:p:361-372
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    Cited by:

    1. Kim, Soohun & Skoulakis, Georgios, 2018. "Ex-post risk premia estimation and asset pricing tests using large cross sections: The regression-calibration approach," Journal of Econometrics, Elsevier, vol. 204(2), pages 159-188.
    2. Mahesh Karra & David Canning & Ryoko Sato, 2020. "Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1251-1268, November.
    3. Xu Ning & Francis K. C. Hui & Alan H. Welsh, 2023. "A double fixed rank kriging approach to spatial regression models with covariate measurement error," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    4. Madior Fall & Muriel Roger, 2008. "L’impact de la réforme de 1990 sur les décisions de départ à la retraite des exploitants agricoles français," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement, INRA Department of Economics, vol. 89(4), pages 29-53.
    5. Kaestner, Robert & Kaushal, Neeraj, 2012. "Effect of immigrant nurses on labor market outcomes of US nurses," Journal of Urban Economics, Elsevier, vol. 71(2), pages 219-229.
    6. Jacob Loree, 2019. "Multidimensional Skill Specialization and Mismatch Over the Lifecycle," 2019 Meeting Papers 892, Society for Economic Dynamics.
    7. Udi Sommer, 2011. "How rational are justices on the Supreme Court of the United States? Doctrinal considerations during agenda setting," Rationality and Society, , vol. 23(4), pages 452-477, November.
    8. Yuanzhang Li, 2018. "Bias Correction of Nonlinear Effect for Longitudinal Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(3), pages 56-60, October.
    9. Stoklosa, Jakub & Dann, Peter & Huggins, Richard M. & Hwang, Wen-Han, 2016. "Estimation of survival and capture probabilities in open population capture–recapture models when covariates are subject to measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 74-86.

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