Research Article
Bayesian estimation of Marshall–Olkin extended exponential parameters under various approximation techniques
Abstract
In this paper, we propse Bayes estimators of the parameters of
Marshall Olkin extended exponential distribution (MOEED) introduced by Marshall-Olkin [2] for complete sample under squared
error loss function (SELF). We have used different approximation
techniques to obtain the Bayes estimate of the parameters. A
Monte Carlo simulation study is carried out to compare the performance of proposed estimators with the corresponding maximum
likelihood estimator (MLE’s) on the basis of their simulated risk.
A real data set has been considered for illustrative purpose of the study.
Marshall Olkin extended exponential distribution (MOEED) introduced by Marshall-Olkin [2] for complete sample under squared
error loss function (SELF). We have used different approximation
techniques to obtain the Bayes estimate of the parameters. A
Monte Carlo simulation study is carried out to compare the performance of proposed estimators with the corresponding maximum
likelihood estimator (MLE’s) on the basis of their simulated risk.
A real data set has been considered for illustrative purpose of the study.
Keywords
Bayes estimator Squared error loss function Lindley’s approximation method T-K approximation MCMC method
References
- . . .
There are 1 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Statistics |
| Authors | |
| Publication Date | April 1, 2014 |
| Published in Issue | Year 2014 Volume: 43 Issue: 2 |