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Classical and Bayesian Statistical Inference of Extropy Measures for the Inverse Garhy Distribution |
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PP: 1491-1510 |
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doi:10.18576/amis/200605
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Author(s) |
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Mohammed Elgarhy,
Ghareeb A. Marei,
Doaa Basalamah,
Said G. Nassr,
Ahmed W. Shawki,
Ramadan Mohamed,
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Abstract |
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| In this article, we develop and investigate a new one-parameter distribution called the inverse Garhy distribution (IGD). One of the cooler things in this article is the treatment of extropy based on uncertainty measures. Extropy, weighted extropy, residual extropy, weighted residual extropy, past extropy, and weighted past extropy are computed in closed-form. Some important statistical and mathematical properties of the newly defined IGD, including mode, quantile function, moments, mean, incomplete moments, inverse moments, and stress-strength reliability, are explored and computed. Statistical inference for unknown parameter and extropy measures is performed using maximum likelihood, maximum product spacing, and Bayesian methods. The IGD is trained on a medical dataset and compared against some well-known inverse statistical distributions using likelihood-based criteria and goodness-of-fit statistics. The IGD outperformed all other models. The IGD is analytically tractable, performs well on real data, and supplies a valuable new tool for modeling positively skewed lifetime data and investigating extropy-based uncertainty in survival and reliability analysis. |
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