Bayesian Estimation for the Generalized Logistic Distribution Type-II Censored Accelerated Life Testing
نویسنده
چکیده
distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract This paper develops Bayesian analysis for Constant Stress Accelerated Life Test (CSALT) under Type-II censoring scheme. Failure times are assumed to distribute as the three-parameter Generalized Logistic (GL) distribution. The inverse power law model is used to represent the relationship between the stress and the scale parameter of a test unit. Bayes estimates are obtained using Markov Chain Monte Carlo (MCMC) simulation algorithm based on Gibbs sampling. Then, confidence intervals, and predicted values of the scale parameter and the reliability function under usual conditions are obtained. Numerical illustration and an illustrative example are addressed for illustrating the theoretical results. WinBUGS software package is used for implementing Markov Chain Monte Carlo (MCMC) simulation and Gibbs sampling .
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