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Nonparametric estimators of the bivariate survival function under random censoring

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  • M. J. Van Der Laan

Abstract

A large number of proposals for estimating the bivariate survival function under random censoring have been made. In this paper we discuss the most prominent estimators, where prominent is meant in the sense that they are best for practical use; Dabrowska's estimator, the Prentice–Cai estimator, Pruitt's modified EM‐estimator, and the reduced data NPMLE of van der Laan. We show how these estimators are computed and present their intuitive background. The asymptotic results are summarized. Furthermore, we give a summary of the practical performance of the estimators under different levels of dependence and censoring based on extensive simulation results. This leads also to a practical advise.

Suggested Citation

  • M. J. Van Der Laan, 1997. "Nonparametric estimators of the bivariate survival function under random censoring," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 51(2), pages 178-200, July.
    • Handle: RePEc:bla:stanee:v:51:y:1997:i:2:p:178-200
    DOI: 10.1111/1467-9574.00049
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    Cited by:

    1. Svetlana K. Eden & Chun Li & Bryan E. Shepherd, 2022. "Nonparametric estimation of Spearman's rank correlation with bivariate survival data," Biometrics, The International Biometric Society, vol. 78(2), pages 421-434, June.
    2. Pao-sheng Shen, 2014. "Simple nonparametric estimators of the bivariate survival function under random left truncation and right censoring," Computational Statistics, Springer, vol. 29(3), pages 641-659, June.

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