Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18847
Title: On a proper way to select population failure distribution and a stochastic optimization method in parameter estimation
Authors: Pang, WK
Hou, SH
Yu, WT
Keywords: Gibbs sampler
Markov chain Monte Carlo
Maximum likelihood estimate
Optimization method
Weibull distribution
Issue Date: 2007
Publisher: Elsevier
Source: European journal of operational research, 2007, v. 177, no. 1, p. 604-611 How to cite?
Journal: European journal of operational research 
Abstract: 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.
URI: http://hdl.handle.net/10397/18847
ISSN: 0377-2217
EISSN: 1872-6860
DOI: 10.1016/j.ejor.2005.11.013
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