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Statistical inference for a repairable system subject to shocks: classical vs. Bayesian.
- Source :
-
Journal of Statistical Computation & Simulation . Jan2020, Vol. 90 Issue 1, p112-137. 26p. - Publication Year :
- 2020
-
Abstract
- Consider a repairable system subject to shocks that arrive according to a non-homogeneous Poisson process (NHPP). As a shock occurs, two types of failure may be happened. Type-I failure occurs with probability q and is rectified by a minimal repair, whereas type-II failure takes place with probability p = 1−q and is removed by replacement. The system is replaced at the nth type I failure or at type II failure, whichever comes first. In the present paper, we find a general representation for the likelihood function of the proposed model. Then, we follow both classical and Bayesian procedures to estimate the model parameters when the time to first failure is a Weibull distribution. Because the Bayesian estimation cannot be obtained in a closed form, we use two approximation methods: Lindley's approximation and MCMC method. Finally, a Monte Carlo simulation is conducted to compare the performance of estimators in classical and Bayesian procedures. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00949655
- Volume :
- 90
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Statistical Computation & Simulation
- Publication Type :
- Academic Journal
- Accession number :
- 139682795
- Full Text :
- https://doi.org/10.1080/00949655.2019.1673392