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Smooth Estimation of Uniformly Stochastically Ordered Survival Functions
Journal of Combinatorics, Information and System Sciences (2006)
  • Yogendra P. Chaubey
  • Subhash Kochar, Portland State University

Let S and T denote two survival functions, such that the func- tion µ(x) = S(x)=T(x) is non-increasing on the support of T. In this case S is called uniformly stochastically smaller than T: This con- cept is useful in reliability and life testing as it is equivalent to the hazard ordering. Rojo and Samaniego (1991, 1993) study consistent estimation of S under uniform stochastic ordering where as Mukerjee (1996) considered the general problem of estimating S and T along the lines considered in Rojo and Samaniego (1991). The delicate mat- ter of the asymptotic distribution of estimators in question has been recently tackled by Arcones and Samaniego (2000). When the un- derlying survival functions are assumed absolutely continuous, there is naturally an interest in flnding smooth estimators, which are not available through the above estimators. Their smooth version through the popular smoothing methods do not guarantee the stochastic or- dering property in the resulting estimators. In this paper we consider an adaptation of the smoothing technique introduced in Chaubey and Sen (1996) and show that the smooth estimator kind of dove tails the non-smooth estimator and as such the strong and weak convergence properties of the previous estimators are maintained.

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Yogendra P. Chaubey and Subhash Kochar. "Smooth Estimation of Uniformly Stochastically Ordered Survival Functions" Journal of Combinatorics, Information and System Sciences (2006)
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