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Posterior Distribution and Loss Functions for Parameter Estimation in Weibull Processes
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Jürgen Franz
Veröffentlicht/Copyright:
15. März 2010
Abstract
Counting processes are used in modelling of repairable systems. In Bayesian parameter inference the choice of fitted prior distributions and loss functions has great importance. This paper deals with studies of prior and posterior densities. Simulation enables comparisons of estimators obtained with different loss functions. Especially, in the case of a Weibull process, properties relative to prior and posterior distributions are investigated.
Published Online: 2010-03-15
Published in Print: 2006-April
© Heldermann Verlag
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- A Practical Procedure for Developing p-Charts
- Posterior Distribution and Loss Functions for Parameter Estimation in Weibull Processes
- Combined CUSUM–Shewhart Schemes for Binomial Data
- A Goodness of Fit Approach to NBURFR and NBARFR Classes
- Control Charts for the Log-Logistic Distribution
- Stability Index of Stochastic Processes: The Statistical Process Control Approach
- Multivariate Max-Chart
- An Optimization Methodology for Condition Based Minimal and Major Preventive Maintenance
- Multivariate Diagnostic Tests: A Review of Different Methods
- A Note on the Concept of Independence
Artikel in diesem Heft
- Transient Analysis of Multistage Degraded Systems with Partial Repairs of General Distribution Modeled by Erlang-k Distribution
- A Practical Procedure for Developing p-Charts
- Posterior Distribution and Loss Functions for Parameter Estimation in Weibull Processes
- Combined CUSUM–Shewhart Schemes for Binomial Data
- A Goodness of Fit Approach to NBURFR and NBARFR Classes
- Control Charts for the Log-Logistic Distribution
- Stability Index of Stochastic Processes: The Statistical Process Control Approach
- Multivariate Max-Chart
- An Optimization Methodology for Condition Based Minimal and Major Preventive Maintenance
- Multivariate Diagnostic Tests: A Review of Different Methods
- A Note on the Concept of Independence
