Quantile estimation for a progressively censored exponential distribution. Issue 16 (17th August 2020) - Mani Tripathi, Yogesh; Petropoulos, Constantinos; Sen, Tanmay
Abstract: In this paper, we consider the problem of estimating the quantile of a two-parameter exponential distribution with respect to an arbitrary strictly convex loss function under progressive type II censoring. Inadmissibility of the best affine equivariant (BAE) estimator is established through a conditional risk analysis. In particular we provide dominance results for quadratic, linex and absolute value loss functions. Further, a class of dominating estimators is derived using the IERD (integral expression of risk difference) approach of Kubokawa (1994 ). In sequel the generalized Bayes estimator is shown to improve the BAE estimator.