Bayesian and non-Bayesian inference under adaptive type-II progressive censored sample with exponentiated power Lindley distribution. Issue 12 (10th September 2022)

• Record Type: Journal Article
• Authors: Haj Ahmad, Hanan; Salah, Mukhtar M.; Eliwa, M. S.; Ali Alhussain, Ziyad; Almetwally, Ehab M.; Ahmed, Essam A.
• Is Part Of: Journal of applied statistics. Volume 49:Issue 12(2022)
• Page Start: 2981
• Page End: 3001
• Publication Date: 2022-09-10
• Subjects: Exponentiated power Lindley -- adaptive progressive type-II censoring -- Bayesian estimation -- maximum likelihood estimator -- Markov chain Monte Carlo -- simulation; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2021.1931819
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: This paper deals with the statistical inference of the unknown parameters of three-parameter exponentiated power Lindley distribution under adaptive progressive type-II censored samples. The maximum likelihood estimator (MLE) cannot be expressed explicitly, hence approximate MLEs are conducted using the Newton–Raphson method. Bayesian estimation is studied and the Markov Chain Monte Carlo method is used for computing the Bayes estimation. For Bayesian estimation, we consider two loss functions, namely: squared error and linear exponential (LINEX) loss functions, furthermore, we perform asymptotic confidence intervals and the credible intervals for the unknown parameters. A comparison between Bayes estimation and the MLE is observed using simulation analysis and we perform an optimally criterion for some suggested censoring schemes by minimizing bias and mean square error for the point estimation of the parameters. Finally, a real data example is used for the illustration of the goodness of fit for this model.