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Estimation and prediction on power Muth distribution with progressive censored data: a Bayesian approach.
- Source :
-
Communications in Statistics: Simulation & Computation . 2024, Vol. 53 Issue 5, p2150-2172. 23p. - Publication Year :
- 2024
-
Abstract
- In this paper, estimation and prediction inference of power Muth distribution, with the progressive censoring data, are described. The maximum likelihood and Bayesian approaches of the unknown parameters are considered. Several Bayesian estimators are obtained against different symmetric and asymmetric loss functions, such as squared error, linex and general entropy. Also, the asymptotic confidence intervals and highest posterior density credible intervals of them are derived. Most focus of this paper is Bayesian prediction of the removed units in multiple stages of the progressively censored sample, so that, the Gibbs and Metropolis samplers are used, to reach this end. To compare the performance of different methods, Mont Carlo simulation is employed. Moreover, one practical data set is analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03610918
- Volume :
- 53
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Simulation & Computation
- Publication Type :
- Academic Journal
- Accession number :
- 177672963
- Full Text :
- https://doi.org/10.1080/03610918.2022.2069259