Markov Chain Monte Carlo Approach for Burr Type XII Distribution  
Author Jianping Zhu
Co-Author(s) Hua Xin; Junge Sun; Tzong-Ru Tsai
Abstract A Markov chain Monte Carlo procedure for the parameter estimation of three-parameter Burr type XII (3pBurrXII) distribution is analytically derived using the Metropolis-Hastings algorithm, and named the M-H MCMC method. The proposed M-H MCMC method is operational for practitioners and can be used to obtain reliable maximum likelihood estimates or Bayes estimates of the 3pBurrXII distribution parameters. The application of the proposed M-H MCMC method is illustrated with an example about the lifetimes of oil-well pumps.
Keywords Gamma distribution, Gibbs sampling, important sampling, Markov chain Monte Carlo method, maximum likelihood estimation
    Article #:  23-020
Proceedings of the 23rd ISSAT International Conference on Reliability and Quality in Design
August 3-5, 2017 - Chicago, Illinois, U.S.A.