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A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator

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  • Qiu, Zhiping
  • Chen, Xiaoping
  • Zhou, Yong

Abstract

In this paper, a nonparametric imputation method is developed for the additive hazards model when the censoring indicator is missing at random (MAR). The asymptotic properties of the proposed estimator are derived and the performance of the proposed estimator is demonstrated by a numerical simulation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:98:y:2015:i:c:p:89-97
    DOI: 10.1016/j.spl.2014.12.006
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    References listed on IDEAS

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    1. Chen M-H. & Ibrahim J.G. & Shao Q-M., 2004. "Propriety of the Posterior Distribution and Existence of the MLE for Regression Models With Covariates Missing at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 421-438, January.
    2. Subramanian, Sundarraman & Bandyopadhyay, Dipankar, 2010. "Doubly robust semiparametric estimation for the missing censoring indicator model," Statistics & Probability Letters, Elsevier, vol. 80(7-8), pages 621-630, April.
    3. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504.
    4. Marc Aerts, 2002. "Local multiple imputation," Biometrika, Biometrika Trust, vol. 89(2), pages 375-388, June.
    5. Qi, Lihong & Wang, C.Y. & Prentice, Ross L., 2005. "Weighted Estimators for Proportional Hazards Regression With Missing Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1250-1263, December.
    6. 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.
    7. Guozhi Gao & Anastasios A. Tsiatis, 2005. "Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure," Biometrika, Biometrika Trust, vol. 92(4), pages 875-891, December.
    8. Zhou, Yong & Wan, Alan T. K & Wang, Xiaojing, 2008. "Estimating Equations Inference With Missing Data," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1187-1199.
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    Cited by:

    1. Shen, Yu & Liang, Han-Ying, 2018. "Quantile regression for partially linear varying-coefficient model with censoring indicators missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 1-18.

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