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On errors-in-variables for binary regression models
Abstract
We consider binary regression models when some of the predictors are measured with error. For normal measurement errors, structural maximum likelihood estimates are considered. We show that if the measurement error is large, the usual estimate of the probability of the event in question can be substantially in error, especially for high risk groups. In the situation of large measurement error, we investigate a conditional maximum likelihood estimator and its properties.
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Cited by
Exposure measurement error in time-series studies of air pollution: concepts and consequences
Zeger, S. L., Thomas, D., Dominici, F., Samet, J. M., Schwartz, J., Dockery, D., & Cohen, A. (2000) · Environmental Health Perspectives · doi:10.1289/ehp.00108419 · Cited by 1,156
Reflection on modern methods: five myths about measurement error in epidemiological research
van Smeden, M., Lash, T. L., & Groenwold, R. H. H. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyz251 · Cited by 173
Stefanski, L. A. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474347 · Cited by 99
Metadata
- Authors
- CARROLL, R. J., SPIEGELMAN, C. H., LAN, K. K. G., BAILEY, K. T., & ABBOTT, R. D.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1984
- Volume
- 71
- Issue
- 1
- Pages
- 19-25
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2080252742
- Cited by
- 219
- References
- 0
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: On errors-in-variables for binary regression models. https://app.cochat.ai/papers/10.1093/biomet/71.1.19
- The paper
- CARROLL, R. J., SPIEGELMAN, C. H., LAN, K. K. G., BAILEY, K. T., & ABBOTT, R. D. (1984). On errors-in-variables for binary regression models. Biometrika. https://doi.org/10.1093/biomet/71.1.19
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