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<p>We propose the new two-parameter Exponentiated Zeghdoudi (EZ) distribution by extending the Zeghdoudi distribution and discuss some of its mathematical and statistical characteristics, such as moments, skewness, kurtosis, etc. Discussed seven methods of estimation and found that MLE outperforms well in terms of estimation. Also, a regression framework based on the logarithmic approach has been carried out to check the model efficiency in biomedical studies and reliability engineering areas. In addition, we carry out Bayesian estimation using the Metropolis–Hastings algorithm. A simulation study has been carried out to validate the estimates obtained using several methods. Finally, five datasets have been taken into consideration to check the applicability of the model in real life scenario by comparing with other lifetime distributions. It is found that our proposed model is much superior and more flexible in fitting the datasets.</p>

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The Exponentiated Zeghdoudi distribution: properties, simulations, regression, and applications

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Molay Kumar Ruidas,
  • M. I. Khan,
  • Sthitadhi Das,
  • Loai M. A. Alzoubi

摘要

We propose the new two-parameter Exponentiated Zeghdoudi (EZ) distribution by extending the Zeghdoudi distribution and discuss some of its mathematical and statistical characteristics, such as moments, skewness, kurtosis, etc. Discussed seven methods of estimation and found that MLE outperforms well in terms of estimation. Also, a regression framework based on the logarithmic approach has been carried out to check the model efficiency in biomedical studies and reliability engineering areas. In addition, we carry out Bayesian estimation using the Metropolis–Hastings algorithm. A simulation study has been carried out to validate the estimates obtained using several methods. Finally, five datasets have been taken into consideration to check the applicability of the model in real life scenario by comparing with other lifetime distributions. It is found that our proposed model is much superior and more flexible in fitting the datasets.