应用于指数分布的新分布族

Layla Abdul, Jaleel Mohsin, Hazim Ghdhaib Kalt
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引用次数: 0

摘要

本文介绍了一种名为阿尔法对数族的新连续分布族,这是一种用于拟合单变量连续分布数据的新建模策略。这是通过引入一个额外参数来实现的,使用单参数自然对数变换可以提高一些父连续分布的建模能力,从而获得更大的灵活性:将这一技术应用于指数分布以获得新的双参数分布,并观察指数分布所发生的变化。还推导和研究了新分布的一般性质和函数,并推导出了两个参数的估计值。通过模拟研究验证了估计器的效率。我们还将新分布应用于两组真实数据,以证明新变换的益处,结果表明,在所选数据上,所提出的模型优于与之比较的渐近分布。
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A New Family of Distributions with an Application to Exponentially Distribution
This paper introduces a new continuous distribution family called the Alpha logarithm family, which is a new modelling strategy for fitting data subject to univariate continuous distributions. This is achieved by introducing an additional parameter for greater flexibility using a single-parameter Natural logarithm transformation which can enhance some of the modeling capabilities of some Parental Continuous Distributions: This technique was applied to the exponential distribution to obtain a new two-parameter distribution, and the changes that occurred in the exponential distribution were observed. The general properties and functions of the new distribution were also derived and studied, and the estimators of the two parameters were derived. The efficiency of the estimators is verified through the simulation study. The new distribution is also applied to two sets of real data to prove the benefit of the new transformation, and we show that the proposed model is better than the asymptotic distributions with which it was compared on the selected data.
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