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Probability density estimation for survival data with censoring indicators missing at random

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  • Wang, Qihua
  • Liu, Wei
  • Liu, Chunling

Abstract

In this paper, some nonparametric approaches of density function estimation are developed when censoring indicators are missing at random. A conditional mean score based estimator and a mean score estimator are suggested, respectively. The two estimators are proved to be asymptotically normal and uniformly strongly consistent. The bandwidth selection problem is also discussed. A simulation study is conducted to compare finite-sample behaviors of the proposed estimators.

Suggested Citation

  • Wang, Qihua & Liu, Wei & Liu, Chunling, 2009. "Probability density estimation for survival data with censoring indicators missing at random," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 835-850, May.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:5:p:835-850
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    References listed on IDEAS

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    1. Anastasios A. Tsiatis, 2002. "Multiple imputation methods for testing treatment differences in survival distributions with missing cause of failure," Biometrika, Biometrika Trust, vol. 89(1), pages 238-244, March.
    2. Diehl, Sabine & Stute, Winfried, 1988. "Kernel density and hazard function estimation in the presence of censoring," Journal of Multivariate Analysis, Elsevier, vol. 25(2), pages 299-310, May.
    3. Lo, Shaw-Hwa, 1991. "Estimating a survival function with incomplete cause-of-death data," Journal of Multivariate Analysis, Elsevier, vol. 39(2), pages 217-235, November.
    4. 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.
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    Cited by:

    1. Yu-Ye Zou & Han-Ying Liang, 2020. "CLT for integrated square error of density estimators with censoring indicators missing at random," Statistical Papers, Springer, vol. 61(6), pages 2685-2714, December.

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