Estimating quantiles of a selected exponential population

Somesh Kumar and Aditi Kar

Statistics & Probability Letters, 2001, vol. 52, issue 1, 9-19

Abstract: Suppose independent random samples are available from k exponential populations with a common location [theta] and scale parameters [sigma]1,[sigma]2,...,[sigma]k, respectively. The population corresponding to the largest sample mean is selected. The problem is to estimate a quantile of the selected population. In this paper, we derive the uniformly minimum variance unbiased estimator (UMVUE) using (U-V) method of Robbins (in: Gupta and Berger (Eds.), Statistical Decision Theory and Related Topics -- IV, Vol. 1, Springer, New York, 1987, pp. 265-270) and Rao-Blackwellization. Further, a general inadmissibility result for affine equivariant estimators is proved. As a consequence, an estimator improving upon the UMVUE with respect to the squared error and the scale invariant loss functions is obtained.

Keywords: Selection; rule; Quantiles; Uniformly; minimum; variance; unbiased; estimator; Affine; equivariant; estimator; Inadmissible; estimator (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (3)

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