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Correcting bias due to misclassification in the estimation of logistic regression models

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  • Cheng, K. F.
  • Hsueh, H. M.

Abstract

This paper describes several properties of some bias correction methods in the estimation of logistic regression models with misclassification in the binary responses. The observation error model consists of a primary data set plus a smaller validation set. The large sample properties of different bias correction methods are compared under various situations, and the asymptotic relative efficiencies of some important methods are derived. Our small sample simulation studies conclude that the semiparametric estimation method considered by Pepe (Biometrika 79 (1992) 355-365) is quite reliable under a reasonable surrogate classifier.

Suggested Citation

  • Cheng, K. F. & Hsueh, H. M., 1999. "Correcting bias due to misclassification in the estimation of logistic regression models," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 229-240, September.
    • Handle: RePEc:eee:stapro:v:44:y:1999:i:3:p:229-240
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    References listed on IDEAS

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    1. Kwanchai Assakul & Charles Proctor, 1967. "Testing independence in two-way contingency tables with data subject to misclassification," Psychometrika, Springer;The Psychometric Society, vol. 32(1), pages 67-76, March.
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    Cited by:

    1. Shen-Ming Lee & Chin-Shang Li & Shu-Hui Hsieh & Li-Hui Huang, 2012. "Semiparametric estimation of logistic regression model with missing covariates and outcome," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(5), pages 621-653, July.
    2. Hsieh, Shu-Hui & Li, Chin-Shang & Lee, Shen-Ming, 2013. "Logistic regression with outcome and covariates missing separately or simultaneously," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 32-54.

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