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<p>This article studies step-stress partially accelerated life test model assuming generalized exponential distribution as life time model. Two approaches of estimation, maximum likelihood (ML) and Markov Chain Monte Carlo (MCMC), are used to make inferences on the model parameters. Mean squared errors are obtained to assess the performance of estimators. Moreover, the confidence bounds with their coverage probabilities are also obtained. Finally, illustrative examples are made to demonstrate the theoretical results and explore the performance of the proposed methods.</p>

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Reliability Analysis of Accelerated Life Test Data from Generalized Exponential Distribution: MCMC Approach

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Ali A. Ismail

摘要

This article studies step-stress partially accelerated life test model assuming generalized exponential distribution as life time model. Two approaches of estimation, maximum likelihood (ML) and Markov Chain Monte Carlo (MCMC), are used to make inferences on the model parameters. Mean squared errors are obtained to assess the performance of estimators. Moreover, the confidence bounds with their coverage probabilities are also obtained. Finally, illustrative examples are made to demonstrate the theoretical results and explore the performance of the proposed methods.