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A note on corrected scores for logistic regression

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  • Buzas, Jeffrey S.

Abstract

This article describes a method for constructing a corrected score for logistic regression that is effective in reducing bias incurred through covariate measurement error. Stefanski (1989) showed there does not exist a corrected likelihood score for the logistic model with normal additive measurement error. To circumvent this result, an approximate likelihood score is defined for the logistic model that is conceptually and computationally straightforward and that is nearly efficient in the absence of measurement error. It is shown that the approximate likelihood score admits a corrected score. The results are extended to the Probit regression model.

Suggested Citation

  • Buzas, Jeffrey S., 2009. "A note on corrected scores for logistic regression," Statistics & Probability Letters, Elsevier, vol. 79(22), pages 2351-2358, November.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:22:p:2351-2358
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    References listed on IDEAS

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    1. Thomas Augustin, 2004. "An Exact Corrected Log‐Likelihood Function for Cox's Proportional Hazards Model under Measurement Error and Some Extensions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 43-50, March.
    2. Novick S.J. & Stefanski L.A., 2002. "Corrected Score Estimation via Complex Variable Simulation Extrapolation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 472-481, June.
    3. Buzas, J. S. & Stefanski, L. A., 1996. "A note on corrected-score estimation," Statistics & Probability Letters, Elsevier, vol. 28(1), pages 1-8, June.
    4. N/A, 1996. "Note:," Foreign Trade Review, , vol. 31(1-2), pages 1-1, January.
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    Cited by:

    1. David M. Zucker & Malka Gorfine & Yi Li & Mahlet G. Tadesse & Donna Spiegelman, 2013. "A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error," Biometrics, The International Biometric Society, vol. 69(1), pages 80-90, March.

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