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<p>In this paper, we propose a new two-parameter lifetime distribution, called the generalized inverted Xgamma exponential distribution, which extends the Xgamma exponential distribution. The proposed model provides enhanced flexibility for modeling diverse data structures and is particularly suitable for characterizing upside-down bathtub type hazard rate patterns, commonly observed in reliability and survival analysis. We derive its key analytical properties, including moments, moment-generating function, quantile function, and stochastic ordering results. Parameter estimation is addressed using both classical and Bayesian approaches, with methodological adaptations for doubly type-II censored data. The finite-sample performance of the proposed estimators is assessed via an extensive Monte Carlo simulation study. Finally, two real-world datasets from the survival and reliability domains are analyzed to illustrate the practical applicability and robustness of the proposed model.</p>

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Statistical inference and applications of the generalized inverted Xgamma exponential distribution using doubly type-II censored data

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Abhimanyu Singh Yadav,
  • Neha Jaiswal,
  • Suraj Yadav

摘要

In this paper, we propose a new two-parameter lifetime distribution, called the generalized inverted Xgamma exponential distribution, which extends the Xgamma exponential distribution. The proposed model provides enhanced flexibility for modeling diverse data structures and is particularly suitable for characterizing upside-down bathtub type hazard rate patterns, commonly observed in reliability and survival analysis. We derive its key analytical properties, including moments, moment-generating function, quantile function, and stochastic ordering results. Parameter estimation is addressed using both classical and Bayesian approaches, with methodological adaptations for doubly type-II censored data. The finite-sample performance of the proposed estimators is assessed via an extensive Monte Carlo simulation study. Finally, two real-world datasets from the survival and reliability domains are analyzed to illustrate the practical applicability and robustness of the proposed model.