Abstract
In this paper, estimations problem (point and interval) are studied for the logarithmic transformed exponential (LTE) distribution based on an adaptive progressive Type-II (APTII ) censoring scheme. In point estimation; the maximum likelihood estimate (MLE) and Bayesian estimates (BE) based on squared error and Linex loss functions are obtained. The Markov Chain Monte Carlo (MCMC) technique is used to compute Bayes estimates by using gamma prior for the unknown parameter under two different loss functions. Metropolis-Hasting (M-H) algorithm has been applied to generate MCMC samples from the posterior density. In case of interval estimation; asymptotic confidence interval (CI) and two different bootstrap CI’s namely; Boot-p and Boot-t are obtained. Also, we have computed posterior credible interval for the unknown parameter. The performance of these estimates are studied on the basis of their risks. Finally, a real dataset has been taken for different censoring schemes and times under APTII censoring.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.