• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.25-32
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• 2003

In this paper, we define the mean life process for the right censored data and show the asymptotic equivalence between two kinds of the mean life processes. We use the Kaplan-Meier and Susarla-Van Ryzin estimates as the estimates of survival function for the construction of the mean life processes. Also we show the asymptotic equivalence between two mean residual life processes as an application and finally discuss some difficulties caused by the censoring mechanism.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.783-795
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• 2005

We consider to obtain an estimate of bivariate survival function for the right censored data with the assumption that the two components of censoring vector are independent. The estimate is derived from an ad hoc approach based on the representation of survival function. Then the resulting estimate can be considered as an extension of the Susarla- Van Ryzin estimate to the bivariate data. Also we show the consistency and weak convergence for the proposed estimate. Finally we compare our estimate with Dabrowska's estimate with an example and discuss some properties of our estimate with brief comment on the extension to the multivariate case.

• The Pure and Applied Mathematics
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• v.2 no.2
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• pp.83-89
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• 1995

A Bayes estimator of the survival distribution function due to Susarla and Van Ryzin(1976) is used to estimate the mth moment of a survival time on the basis of censored observations in a random censorship model. Asymptotic normality of the estimator is proved using the functional version of the delta method.