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Applicant:
Indian Institute of Technology (IIT) Patna 
Author:
Yogesh Mani Tripathi, C. Petropoulos and Tanmay Sen  
Corresponding Authors:
Constantinos Petropoulos 
DOI #:
https://doi.org/10.1080/03610926.2019.1593456 
Title:
Quantile estimation for a progressively censored exponential distribution.  
Journal:
Communications in Statistics: Theory and Methods 
Year:
2020 
Volume:
49 
Page:
3919-3932 
Keywords:
Progressive censoring; quantile; inadmissible estimator; equivariant estimator 
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 
Entered by:
Physics Head on 2020-08-04 
 
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