Statistical Inference of Two Gamma Distributions under the Joint Type-II Censoring Scheme

• Main Authors: Ding, L. (Author), Gui, W. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: Bayesian estimates; Gamma distribution; joint Type-II censoring; maximum likelihood estimators; Metropolis–Hastings algorithm; Monte Carlo simulations
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 22277390 (ISSN)
• DOI: 10.3390/math11092003

The joint Type-II censoring scheme is a useful model when carrying out comparative lifecycle tests of units from various production lines. This article takes into account the estimation problem of the joint Type-II censoring data coming from two Gamma distributions with the same shape parameter but various scale parameters. The maximum likelihood estimators of the parameters from Gamma populations and asymptotic confidence intervals based on the observed Fisher information matrix are obtained. Bootstrap methods are also applied to construct confidence intervals. The Metropolis–Hastings algorithm is considered to draw Markov Chain Monte Carlo samples when computing Bayesian estimates as well as establishing the corresponding credible intervals. Monte Carlo simulations are adopted to compare the performance of the estimates. Finally, two real engineering datasets are analyzed. © 2023 by the authors.