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On the Asymptotic Distribution Theory of a Class of Consistent Estimators of a Distribution Satisfying a Uniform Stochastic Ordering Constraint

Miguel A. Arcones and Francisco J. Samaniego
The Annals of Statistics
Vol. 28, No. 1 (Feb., 2000), pp. 116-150
Stable URL: http://www.jstor.org/stable/2673984
Page Count: 35
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
On the Asymptotic Distribution Theory of a Class of Consistent Estimators of a Distribution Satisfying a Uniform Stochastic Ordering Constraint
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Abstract

We identify the asymptotic behavior of the estimators proposed by Rojo and Samaniego and Mukerjee of a distribution F assumed to be uniformly stochastically smaller than a known baseline distribution G. We show that these estimators are $\sqrt{n}$-convergent to a limit distribution with mean squared error smaller than or equal to the mean squared error of the empirical survival function. By examining the mean squared error of the limit distribution, we are able to identify the optimal estimator within Mukerjee's class under a variety of different assumptions on F and G. Similar theoretical results are developed for the two-sample problem where F and G are both unknown. The asymptotic distribution theory is applied to obtain confidence bands for the survival function F̄ based on published data from an accelerated life testing experiment.

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