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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We prove, by providing three applications to real-life data, that Nakagami Exponential (Nak-E) distribution could give a better fit when compared to its competitors.
📌 3 Key Takeaways
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Objective & Hypothesis:In this article, we proposed a new family of generalized Nak-G distributions and study some of its statistical properties, such as moments, moment generating function, quantile function, and probability Weighted Moments.
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Key Findings & Data:The Renyi entropy, expression of distribution order statistic, and parameters of the model are estimated by means of maximum likelihood technique.
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Conclusion & Impact:We prove, by providing three applications to real-life data, that Nakagami Exponential (Nak-E) distribution could give a better fit when compared to its competitors.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Nak-G · Nak-E · Maximum Likelihood Estimation
Abstract
In this article, we proposed a new family of generalized Nak-G distributions and study some of its statistical properties, such as moments, moment generating function, quantile function, and probability Weighted Moments. The Renyi entropy, expression of distribution order statistic, and parameters of the model are estimated by means of maximum likelihood technique. We prove, by providing three applications to real-life data, that Nakagami Exponential (Nak-E) distribution could give a better fit when compared to its competitors.
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Cite This Article
Abdullahi, İ., Job, O. (2020). A new family of odd generalized Nakagami (Nak-G) distributions. Turkish Journal of Science, Cilt: 5 Sayı: 2, 85–101. https://ajindex.com/articles/1557564