Modified Weibull model: A Bayes study using MCMC approach based on progressive censoring data

In this paper, we investigate the problem of point and interval estimations for the modified Weibull distribution (MWD) using progressively type-II censored sample. The maximum likelihood (ML), Bayes, and parametric bootstrap methods are used for estimating the unknown parameters as well as some lif...

Full description

Saved in:
Bibliographic Details
Published in:Reliability engineering & system safety 2012-04, Vol.100, p.48-57
Main Authors: Soliman, Ahmed A., Abd-Ellah, Ahmed H., Abou-Elheggag, Naser A., Ahmed, Essam A.
Format: Article
Language:English
Subjects:
Applied sciences
Asymmetry
Balanced loss
Balancing
Bayesian analysis
Bayesian estimation
Bootstrap
Estimates
Exact sciences and technology
Gibbs and Metropolis–Hasting samplers
Hybrid MCMC approach
Intervals
Mathematical models
Mathematics
Maximum likelihood estimation
Modified Weibull distribution
Monte Carlo methods
Operational research and scientific management
Operational research. Management science
Parametric inference
Probability and statistics
Progressive type-II censoring
Reliability theory. Replacement problems
Sampling theory, sample surveys
Sciences and techniques of general use
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we investigate the problem of point and interval estimations for the modified Weibull distribution (MWD) using progressively type-II censored sample. The maximum likelihood (ML), Bayes, and parametric bootstrap methods are used for estimating the unknown parameters as well as some lifetime parameters (reliability and hazard functions). Also, we propose to apply Markov chain Monte Carlo (MCMC) technique to carry out a Bayesian estimation procedure. Bayes estimates and the credible intervals are obtained under the assumptions of informative and noninformative priors. The results of Bayes method are obtained under both the balanced squared error loss (bSEL) and balanced linear-exponential (bLINEX) loss. We show that these loss functions are more general, which include the MLE and both symmetric and asymmetric Bayes estimates as special cases. Finally, Two real data sets have been analyzed for illustrative purposes.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2011.12.013