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Logistic regression with outcome and covariates missing separately or simultaneously

Author

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  • Hsieh, Shu-Hui
  • Li, Chin-Shang
  • Lee, Shen-Ming

Abstract

Estimation methods are proposed for fitting logistic regression in which outcome and covariate variables are missing separately or simultaneously. One of the two proposed estimators is an extension of the validation likelihood estimator of Breslow and Cain (1988). The other is a joint conditional likelihood estimator that uses both validation and non-validation data. Large sample properties of the proposed estimators are studied under certain regularity conditions. Simulation results show that the joint conditional likelihood estimator is more efficient than the validation likelihood estimator, weighted estimator, and complete-case estimator. The practical use of the proposed methods is illustrated with data from a cable TV survey study in Taiwan.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:66:y:2013:i:c:p:32-54
    DOI: 10.1016/j.csda.2013.03.007
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    References listed on IDEAS

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    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. Chatterjee, Nilanjan & Li, Yan, 2010. "Inference in Semiparametric Regression Models Under Partial Questionnaire Design and Nonmonotone Missing Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 787-797.
    3. 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.
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    Cited by:

    1. Kim-Hung Pho & Michael McAleer, 2021. "Specification and Estimation of a Logistic Function, with Applications in the Sciences and Social Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(2), pages 74-104, June.
    2. Shen-Ming Lee & Truong-Nhat Le & Phuoc-Loc Tran & Chin-Shang Li, 2023. "Estimation of logistic regression with covariates missing separately or simultaneously via multiple imputation methods," Computational Statistics, Springer, vol. 38(2), pages 899-934, June.
    3. Shu-Hui Hsieh & Shen-Ming Lee & Chin-Shang Li & Su-Hao Tu, 2016. "An alternative to unrelated randomized response techniques with logistic regression analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 601-621, November.

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