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A note on kernel assisted estimators in missing covariate regression

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  • Wang, Suojin
  • Wang, C. Y.

Abstract

We investigate the asymptotic relationships among three kernel assisted semiparametric estimators in regression analysis when some covariates are missing or measured with error. Smoothing techniques are employed in estimating the selection probabilities and the conditionally expected scores, a step which is required to obtain the estimators of interest. The asymptotic distributional properties of these estimators are derived and their asymptotic equivalence is shown. Some important differences are also noted. Furthermore, the asymptotic efficiency of the estimators relative to the usual maximum likelihood estimator is obtained.

Suggested Citation

  • Wang, Suojin & Wang, C. Y., 2001. "A note on kernel assisted estimators in missing covariate regression," Statistics & Probability Letters, Elsevier, vol. 55(4), pages 439-449, December.
  • Handle: RePEc:eee:stapro:v:55:y:2001:i:4:p:439-449
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    References listed on IDEAS

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    1. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 1-21, June.
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    Cited by:

    1. Qiu, Zhiping & Chen, Xiaoping & Zhou, Yong, 2015. "A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 89-97.
    2. Menggang Yu & Bin Nan, 2010. "Regression Calibration in Semiparametric Accelerated Failure Time Models," Biometrics, The International Biometric Society, vol. 66(2), pages 405-414, June.
    3. Lei Jin & Suojin Wang, 2010. "A Model Validation Procedure when Covariate Data are Missing at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 403-421.
    4. Na Hu & Xuerong Chen & Jianguo Sun, 2015. "Regression Analysis of Length-biased and Right-censored Failure Time Data with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 438-452, June.
    5. Creemers, An & Aerts, Marc & Hens, Niel & Molenberghs, Geert, 2012. "A nonparametric approach to weighted estimating equations for regression analysis with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 100-113, January.

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