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- Lin, Jing, et al.
(author)
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A Cure Rate Model in Reliability for Complex System
- 2008
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In: 2008 IEEE International Conference on Industrial Engineering and Engineering Management. - : IEEE Communications Society. - 9781424426300 ; , s. 1395-1399
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Conference paper (peer-reviewed)abstract
- This paper presents a new approach to do reliability analysis for complex system, where a certain fraction of the subsystems is defined as a ¿cure fraction¿ under the consideration that such subsystems are ¿longevous¿ compared with the entire system. Including introducing environment covariates and the joint power prior, the proposed model is developed with the Bayesian survival analysis method, and thus the problems for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameters¿ posterior distribution. Finally, a numeric example is discussed to demonstrate the proposed model.
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- Lin, Jing, et al.
(author)
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Bayesian analysis for randomly truncated constant-stress accelerated life testing
- 2007
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In: Journal of Systems Engineering and Electronics. - 1001-506X. ; 29:2, s. 320-323
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Journal article (peer-reviewed)abstract
- Aimed at the fault of the traditional numeration methods, the Weibull model, which is used widely in the family of Bayesian accelerated failure-time model was discussed. Markov chain Monte Carlo method based on Gibbs sampling was discussed, which were used to simulate dynamically the Markov Chain of the parameters’ posterior distribution. Also, the parameters’ Bayesian estimations were given out with prior suppose for its parameters. What’s more, the results of the data’s simulation were utilized to show the process of setting the model by using the BUGS package. It proves the objectivity and validity of the model.
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- Lin, Jing, et al.
(author)
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Bayesian survival analysis in reliability for complex system with a cure fraction
- 2011
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In: International Journal of Performability Engineering. - 0973-1318. ; 7:2, s. 109-120
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Journal article (peer-reviewed)abstract
- In traditional methods for reliability analysis, one complex system is often considered as being composed by some subsystems in series. Usually, the failure of any subsystem would be supposed to lead to the failure of the entire system. However, some subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. Moreover, such subsystems' lifetimes will not be influenced equally under different circumstances. In practice, such interferences will affect the model's accuracy, but it is seldom considered in traditional analysis. To address these shortcomings, this paper presents a new approach to do reliability analysis for complex systems. Here a certain fraction of the subsystems is defined as a "cure fraction" under the consideration that such subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. By introducing environmental covariates and the joint power prior, the proposed model is developed within the Bayesian survival analysis framework, and thus the problem for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo computational scheme is implemented and a numeric example is discussed to demonstrate the proposed model
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- Lin, Jing, et al.
(author)
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Study on Storage Reliability Evaluation for Ammunition Using Gibbs Sampler
- 2007
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In: Journal of China Ordnance. - 1673-002X. ; , s. 268-271
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Journal article (peer-reviewed)abstract
- For the gradual maturity of Bayesian survival analysis theory, as well as the defects of the traditional methods for storage reliability evaluation, the Bayesian survival analysis method is proposed to build regression models for reliability in the random truncated test. These models can reflect the influences of different environments on the ammunition storage lifetime. As an example, the common exponential distribution is used here, and Markov chain Monte Carlo(MCMC)method based on Gibbs sampling dynamically simulates the Markov chain of the parameters' posterior distribution. Also,the parameters' Bayesian estimations are calculated in the random truncated condition. The simulation results show that the proposed method is effective and directly perceived.
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- Zhu, Huiming, et al.
(author)
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Bayesian Analysis of a Stochastic Volatility Model Using Gibbs Sampling
- 2008
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In: Hunan Daxue Xuebao (Ziran Kexue Ban). - 1674-2974. ; 35:12
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Journal article (peer-reviewed)abstract
- After exploring the statistical structure of the stochastic volatility model, its likelihood functions concrete form is derived, and its parameters conjugate priors are constructed. Then, using the Bayesian theorem, the conditional posterior distributions are derived. In order to obtain the Baysian parameters estimators and their intervals, we develop a Markov chain Monte Carlo algorithm procedure with Gibbs sampling. Finally, using the Shanghai stocks comprehensive index and the Shenzhen stocks composition index, we give an empirical example to illustrate how to use the proposed method. This paper provides a new approach to establish Bayesian models for financial stochastic volatilities with random parameters.
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| 19. |
- Zhu, Huiming, et al.
(author)
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Bayesian multivariate monitoring models for process mean vectors based on multistage predictive distributions
- 2011
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In: Hunan Daxue Xuebao (Ziran Kexue Ban). - 1674-2974. ; 38:3, s. 82-86
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Journal article (peer-reviewed)abstract
- The aim of this paper is to utilize sample information in different stages and solve the parameter uncertainty risk in statistical process control. This paper introduces a reference prior distribution for the parameters in quality models, and constructs the warning lines and action lines to monitor the mean vectors change according to the predictive distribution as well as the relationship between the multivariate t distribution and F distribution. When the current stage is under control, the parametric posterior distribution is considered to be their priori distribution in the next stage, in which the sequential Bayesian mean vector control method is established.
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