eng
Science and Education Publishing
American Journal of Applied Mathematics and Statistics
2328-7292
2019-07-29
7
4
152
160
10.12691/ajams-7-4-5
AJAMS2019745
article
On the Comparison of Classical and Bayesian Methods of Estimation of Reliability in Multicomponent Stress-Strength Model for a Proportional Hazard Rate Model
Taruna Kumari
1
Anupam Pathak
pathakanupam24@gmail.com
2
Department of Statistics, University of Delhi, Delhi-110007, India
Department of Statistics, Ramjas College, University of Delhi, Delhi-110007, India
In this article, we consider a multicomponent stress-strength model which has k independent and identical strength components X1, X2, ..., Xk and each component is exposed to a common random stress Y. Both stress and strength are assumed to have proportional hazard rate model with different unknown power parameters. The system is regarded as operating only if at least s out of k(1≤s≤k) strength variables exceeds the random stress. Reliability of the system is estimated by using maximum likelihood, uniformly minimum variance unbiased and Bayesian methods of estimation. The asymptotic confidence interval is constructed for the reliability function. The performances of these estimators are studied on the basis of their mean squared error through Monte Carlo simulation technique.
http://pubs.sciepub.com/ajams/7/4/5/ajams-7-4-5.pdf
proportional hazard rate model; maximum likelihood estimation
uniformly minimum variance unbiased estimation
Bayesian estimation; asymptotic confidence interval
multicomponent reliability