Parameter Estimation for a Burr XII Distribution under Progressive Censoring. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Maurya, Raj Kamal; Tripathi, Yogesh Mani; Rastogi, Manoj Kumar; Asgharzadeh, A.
• Is Part Of: American journal of mathematical and management sciences. Volume 36:Issue 3(2017)
• Page Start: 259
• Page End: 276
• Publication Date: 2017-07-03
• Subjects: Bayes estimators -- bootstrapping -- EM algorithm -- Fisher information matrix -- importance sampling -- loss function -- Tierney and Kadane method; Operations research -- Periodicals; Management science -- Periodicals; Periodicals; 658.4034
• DOI: 10.1080/01966324.2017.1334604
• Languages: English
• ISSNs: 0196-6324

Abstract

SYNOPTIC ABSTRACT: This article deals with the problem of estimating unknown parameters of a Burr Type XII distribution with the data that are progressive Type-II censored. The maximum likelihood estimators are derived using an EM algorithm. Approximate confidence intervals based on the observed Fisher information matrix and bootstrap intervals of the unknown parameters are obtained. Bayes estimators are derived under different loss functions by making use of the Tierney and Kadane method and importance sampling procedure. Samples obtained from the importance sampling procedure are further used to construct the highest posterior density intervals of unknown parameters. A simulation study is conducted to study the performance of proposed estimators. Finally, a real life data and a simulated data are analyzed for illustration.