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Estimation of Stress-Strength Reliability for Quasi Lindely Distribution
The Journal of Advances in Systems Science and Applications (ASSA). • 2018
Publication Information
Authors
M.M. Mohie El-Din, A. Sadek, Shaimaa H. Elmeghawry
Keywords
Quasi Lindley distribution; Stress-strength reliability; Maximum likelihood
estimation; Asymptotic confidence interval; Bayesian estimation; Importance
sampling technique; MCMC technique via Metropolis-Hastings algorithm
Journal
The Journal of Advances in Systems Science and Applications (ASSA).
Publisher
Not Available
Volume
18(4)
Issue
Not Available
Pages
1–12
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
This paper discussed the problem of estimating of the stress-strength reliability R =
P r(Y < X). It is assumed that the strength of a system X, and the environmental stress applied
on it Y, follow the Quasi Lindley Distribution(QLD). Stress-strength reliability is studied using
the maximum likelihood, and Bayes estimations. Asymptotic confidence interval for reliability is
obtained. Bayesian estimations were proposed using two different methods: Importance Sampling
technique, and MCMC technique via Metropolis-Hastings algorithm, under symmetric loss
function (squared error) and asymmetric loss functions (linex, general entropy). The behaviors
of the maximum likelihood and Bayes estimators of stress-strength reliability have been studied
through the Monte Carlo simulation study.
P r(Y < X). It is assumed that the strength of a system X, and the environmental stress applied
on it Y, follow the Quasi Lindley Distribution(QLD). Stress-strength reliability is studied using
the maximum likelihood, and Bayes estimations. Asymptotic confidence interval for reliability is
obtained. Bayesian estimations were proposed using two different methods: Importance Sampling
technique, and MCMC technique via Metropolis-Hastings algorithm, under symmetric loss
function (squared error) and asymmetric loss functions (linex, general entropy). The behaviors
of the maximum likelihood and Bayes estimators of stress-strength reliability have been studied
through the Monte Carlo simulation study.