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<p>A two-step sequential procedure for estimating the generalized exponential distribution parameters and the acceleration factor is addressed. Maximum likelihood estimates are obtained, and Markov Chain Monte Carlo (MCMC) is then used to perform Bayesian inference on the model parameters. The highest posterior density credible intervals for the model parameters are constructed using the Gibbs sampling method. To assess the performance of the estimates, the mean squared error (MSE) and the confidence limits for the model parameters, along with their coverage probabilities, are calculated via Monte Carlo simulations for comparison purposes.</p>

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Bayesian Estimation of Generalized Exponential Distribution Under Time-Censored Accelerated Life Testing Model Using Gibbs Sampling Procedure

  • 期刊论文

  • 发表日期：

    2025年11月1日

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  • Ali A. Ismail

摘要

A two-step sequential procedure for estimating the generalized exponential distribution parameters and the acceleration factor is addressed. Maximum likelihood estimates are obtained, and Markov Chain Monte Carlo (MCMC) is then used to perform Bayesian inference on the model parameters. The highest posterior density credible intervals for the model parameters are constructed using the Gibbs sampling method. To assess the performance of the estimates, the mean squared error (MSE) and the confidence limits for the model parameters, along with their coverage probabilities, are calculated via Monte Carlo simulations for comparison purposes.