Bayesian Estimation Based on Progressively Type-II Censored Samples from Compound Rayleigh Distribution

DOI

10.2991/jsta.2015.14.2.1How to use a DOI?

Keywords

Compound Rayleigh distribution (CRD); Progressive type-II censoring; Bootstrap methods; Bayesian and non-Bayesian approach; Markov chain Monte Carlo (MCMC); Gibbs and Metropolis sampler

Abstract

This paper considers inference under progressive type-II censoring scheme with a compound Rayleigh failure time distribution. The maximum likelihood (ML) and the Bayes estimators for the two unknown parameters of the compound Rayleigh distribution (CRD) distribution are derived. A Bayesian approach using Markov chain Monte Carlo (MCMC) method to generate from the posterior distributions and in turn computing the Bayes estimators are developed. Point estimation and confidence intervals based on maximum likelihood and bootstrap methods are also proposed. The approximate Bayes estimators have been obtained under the assumptions of informative and non-informative priors. An example with the real data is discussed to illustrate the proposed methods. Finally, we made comparisons between the maximum likelihood and different Bayes estimators using a Monte Carlo simulation study.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Rashad Mohamed EL-Sagheer
AU  - Mohamed Ahsanullah
PY  - 2015
DA  - 2015/06/30
TI  - Bayesian Estimation Based on Progressively Type-II Censored Samples from Compound Rayleigh Distribution
JO  - Journal of Statistical Theory and Applications
SP  - 107
EP  - 122
VL  - 14
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2015.14.2.1
DO  - 10.2991/jsta.2015.14.2.1
ID  - EL-Sagheer2015
ER  -