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An inverse probability weighted estimator for the bivariate distribution function under right censoring

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  • Dai, Hongsheng
  • Bao, Yanchun

Abstract

An inverse probability weighted estimator is proposed for the joint distribution function of bivariate random vectors under right censoring. The new estimator is based on the idea of transformation of bivariate survival functions and bivariate random vectors to univariate survival functions and univariate random variables. The estimator converges weakly to a zero-mean Gaussian process with an easily estimated covariance function. Numerical studies show that the new estimator is more efficient than some existing inverse probability weighted estimators.

Suggested Citation

  • Dai, Hongsheng & Bao, Yanchun, 2009. "An inverse probability weighted estimator for the bivariate distribution function under right censoring," Statistics & Probability Letters, Elsevier, vol. 79(16), pages 1789-1797, August.
  • Handle: RePEc:eee:stapro:v:79:y:2009:i:16:p:1789-1797
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    References listed on IDEAS

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    1. Ross L. Prentice & F. Zoe Moodie & Jianrong Wu, 2004. "Hazard‐based nonparametric survivor function estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 305-319, May.
    2. Michael G. Akritas & Ingrid Van Keilegom, 2003. "Estimation of bivariate and marginal distributions with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 457-471, May.
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    Cited by:

    1. Hongsheng Dai & Marialuisa Restaino & Huan Wang, 2016. "A class of nonparametric bivariate survival function estimators for randomly censored and truncated data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(4), pages 736-751, October.
    2. Zhang, Qiaozhen & Dai, Hongsheng & Fu, Bo, 2016. "A proportional hazards model for time-to-event data with epidemiological bias," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 224-236.

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