PARAMETER ESTIMATION UNDER FAILURE CENSORED CONSTANTSTRESS LIFE TESTING MODEL USING MCMC APPROACH
DOI:
https://doi.org/10.23055/ijietap.2018.25.3.1562Abstract
In this article likelihood and Bayesian estimations for the partially accelerated constant-stress life test model are compared using Type-II censored data from the Pareto distribution of the second kind. The posterior means and posterior variances are obtained under the squared error loss function using Lindley’s approximation procedure. Furthermore, the highest posterior density credible intervals of the model parameters based on Gibbs sampling technique are presented. For illustration, simulation studies are provided. It is shown with the Bayesian approach via Gibbs sampling procedure that the statistical precision of parameter estimation is improved. Consequently, the required number of failures could be reduced. That is, more savings in time and cost can be achieved through the Markov chain Monte Carlo (MCMC) technique. Reducing the total testing time and the total number of failures without sacrificing much of the statistical power in inference is often desired in industrial applications.
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