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Life expectancy under random censorship

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  • Yang, Grace

Abstract

This paper deals with estimation of life expectancy used in survival analysis and competing risk study under the condition that the data are randomly censored by K independent censoring variables. The estimator constructed is based on a theorem due to Berman [2], and it involves an empirical distribution function which is related to the Kaplan-Meier estimate used in biometry. It is shown that the estimator, considered as a function of age, converges weakly to a Gaussian process. It is found that for the estimator to have finite limiting variance requires the assumption that the censoring variables be stochastically larger than the "survival" random variable under investigation.

Suggested Citation

  • Yang, Grace, 1977. "Life expectancy under random censorship," Stochastic Processes and their Applications, Elsevier, vol. 6(1), pages 33-39, November.
    • Handle: RePEc:eee:spapps:v:6:y:1977:i:1:p:33-39
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    Cited by:

    1. Lorenzo, Edgardo & Malla, Ganesh & Mukerjee, Hari, 2013. "Estimation of distributions with the new better than used in expectation property," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1346-1352.

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