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Abstract <p>Accurate statistical modeling is crucial in diverse fields such as engineering and biomedical sciences. In this study, we analyze two real-world datasets one from an engineering domain, representing system reliability data, and another related to chemotherapy and radiation treatment, capturing survival patterns in medical research. Existing distributions often fail to provide an optimal fit for such data sets, necessitating the development of more flexible models. To address this, we propose the transmuted Komal distribution as an extension of the one-parameter Komal distribution and explore its key statistical properties, including moments, the likelihood function, the hazard rate function, maximum likelihood estimation, order statistics, and reliability analysis. The empirical results highlight the effectiveness of the transmuted Komal distribution in capturing complex data structures, making it a valuable addition to the family of generalized probability distributions.</p>

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The Transmuted Komal Distribution: Properties, Estimation, and Real-World Applications

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Suvarna Ranade,
  • Aafaq A. Rather,
  • Maryam Aljarrah,
  • D. Vedavathi Saraja,
  • Syed M. Parveen

摘要

Abstract

Accurate statistical modeling is crucial in diverse fields such as engineering and biomedical sciences. In this study, we analyze two real-world datasets one from an engineering domain, representing system reliability data, and another related to chemotherapy and radiation treatment, capturing survival patterns in medical research. Existing distributions often fail to provide an optimal fit for such data sets, necessitating the development of more flexible models. To address this, we propose the transmuted Komal distribution as an extension of the one-parameter Komal distribution and explore its key statistical properties, including moments, the likelihood function, the hazard rate function, maximum likelihood estimation, order statistics, and reliability analysis. The empirical results highlight the effectiveness of the transmuted Komal distribution in capturing complex data structures, making it a valuable addition to the family of generalized probability distributions.