Article ID: | iaor20084768 |
Country: | Netherlands |
Volume: | 177 |
Issue: | 1 |
Start Page Number: | 604 |
End Page Number: | 611 |
Publication Date: | Feb 2007 |
Journal: | European Journal of Operational Research |
Authors: | Pang Wan-Kai, Hou Shui-Hung, Yu Wing-Tong |
Keywords: | markov processes |
It is widely accepted that the Weibull distribution plays an important role in reliability applications. The reliability of a product or a system is the probability that the product or the system will still function for a specified time period when operating under some confined conditions. Parameter estimation for the three parameter Weibull distribution has been studied by many researchers in the past. Maximum likelihood has traditionally been the main method of estimation for Weibull parameters along with other recently proposed hybrids of optimization methods. In this paper, we use a stochastic optimization method called the Markov Chain Monte Carlo (MCMC) to carry out the estimation. The method is extremely flexible and inference for any quantity of interest is easily obtained.