Selecting the best exponential population under Type-II progressive censoring scheme via empirical Bayes approach. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Golparvar, Leila; Parsian, Ahmad
• Is Part Of: Communications in statistics. Volume 46:Issue 1(2017)
• Page Start: 404
• Page End: 422
• Publication Date: 2017-01-02
• Subjects: Asymptotic optimality -- Best population -- Empirical Bayes rule -- Ranking and selection; 62C12 -- 62F07 -- 62F12; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2014.964806
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: The problem of selecting a population according to "selection and ranking" is an important statistical problem. The ideas in selecting the best populations with some demands having optimal criterion have been suggested originally by Bechhofer (1954 ) and Gupta (1956, 1965 ). In the area of ranking and selection, the large part of literature is connected with a single criterion. However, this may not satisfy the experimenter's demand. We follow methodology of Huang and Lai (1999 ) and the main focus of this article is to select a best population under Type-II progressively censored data for the case of right tail exponential distributions with a bounded and unbounded supports for μ i . We formulate the problem and develop a Bayesian setup with two kinds of bounded and unbounded prior for μ i . We introduce an empirical Bayes procedure and study the large sample behavior of the proposed rule. It is shown that the proposed empirical Bayes selection rule is asymptotically optimal.