Journal
APPLIED MATHEMATICAL MODELLING
Volume 40, Issue 2, Pages 1180-1192Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2015.06.022
Keywords
Exponentiated Weibull distribution; An adaptive Type-II progressive censoring scheme; Bayesian non-Bayesian approaches; Parametric bootstrap confidence intervals; Markov chain Monte Carlo; Importance sampling scheme
Funding
- Institute of Scientific Research and Revival of Islamic Heritage, Umm Al-Qura University [43305016]
Ask authors/readers for more resources
In reliability and life testing experiments, the censoring scheme which can balance between the total time spent for the experiment, the number of units used and the efficiency of statistical inference based on the results of the experiment is desirable. An adaptive Type-II progressive censoring schemes have been shown to be useful in this case. This article deals with the problem of estimating parameters, reliability and hazard functions of the two-parameter exponentiated Weibull distribution, under adaptive progressive Type II censoring samples using Bayesian and non-Bayesian approaches. Maximum likelihood estimates (M LEs) are proposed for unknown quantities. The asymptotic normality of the MLEs are used to compute the approximate confidence intervals for these quantities, parametric bootstrap confidence intervals are also constructed. Markov Chain Monte Carlo (MCMC) samples using importance sampling scheme are used to produce the Bayes estimates and the credible intervals for the unknown quantities. A real-life data-set is analyzed to illustrate the proposed methods of estimation. Finally, results from simulation studies assessing the performance of the maximum likelihood and Bayes estimators are discussed. (C) 2015 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available