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<p>This manuscript introduces the Gamma Exponentiated Generalized-G (GEG-G) family of distributions, developed by integrating the gamma distribution with the exponentiated generalized (EG) family. The resulting class offers enhanced flexibility and can accommodate a broad spectrum of distributional behaviors. Several special cases within the GEG-G family are presented to illustrate its versatility. Parameter estimation is carried out using maximum likelihood point estimation techniques, and the accuracy and efficiency of these estimators are evaluated through a comprehensive Monte Carlo simulation study. To demonstrate practical applicability, a specific member of the GEG-G family is applied to real-world lifetime count datasets.</p>

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Gamma exponentiated generalized family of distributions with properties and applications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月13日

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  • Etaf Alshawarbeh,
  • Boikanyo Makubate,
  • Subhankar Dutta,
  • Regent Retrospect Musekwa

摘要

This manuscript introduces the Gamma Exponentiated Generalized-G (GEG-G) family of distributions, developed by integrating the gamma distribution with the exponentiated generalized (EG) family. The resulting class offers enhanced flexibility and can accommodate a broad spectrum of distributional behaviors. Several special cases within the GEG-G family are presented to illustrate its versatility. Parameter estimation is carried out using maximum likelihood point estimation techniques, and the accuracy and efficiency of these estimators are evaluated through a comprehensive Monte Carlo simulation study. To demonstrate practical applicability, a specific member of the GEG-G family is applied to real-world lifetime count datasets.