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A Simple Corrected Score for Logistic Regression with Errors-in-Covariates

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  • Jian Chen
  • John J. Hanfelt
  • Yijian Huang

Abstract

We develop a simple corrected score for logistic regression with errors-in-covariates. The new method is an extension of the consistent functional methods proposed by Huang and Wang (2001) and is closely related to the corrected score method by Nakamura (1990) and Stefanski (1989). The new method requires that the measurement error distribution is known, but does not require normality. The new method yields a consistent and asymptotically normal estimator under regularity conditions. We examine the finite-sample performance of the new estimator through simulation studies. Finally, we illustrate the new method by applying it to an AIDS study.

Suggested Citation

  • Jian Chen & John J. Hanfelt & Yijian Huang, 2015. "A Simple Corrected Score for Logistic Regression with Errors-in-Covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(10), pages 2024-2036, May.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:10:p:2024-2036
    DOI: 10.1080/03610926.2013.773350
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    Cited by:

    1. Firouzeh Noghrehchi & Jakub Stoklosa & Spiridon Penev, 2020. "Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables," Computational Statistics, Springer, vol. 35(3), pages 1291-1317, September.

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