GDUS修正的Topp-Leone分布:一个具有增加、减少和浴缸危险函数的新分布

Pub Date : 2022-05-07 DOI:10.13052/jrss0974-8024.15112
A. Kaushik, U. Nigam
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引用次数: 1

摘要

在本文中,我们提出了Topp-Leone分布的一个扩展,如[20]使用[8]给出的广义DUS变换引入的。Topp-Leone分布定义在区间(0,1)上,并具有特征J形频率曲线。Topp-Leone分布的新扩展版本适应了各种形状的危险率函数,使其成为一种通用分布。我们还导出了一些性质的显式表达式,如常矩、条件矩、阶统计量分布、分位数、平均偏差和熵。此外,我们还讨论了与两个独立随机变量有关的可识别性、应力强度可靠性和随机排序的结果。为了推断分布的未知参数,我们导出了给出其最大似然估计量的方程。我们还利用Fisher信息矩阵,基于大样本性质,给出了分布的未知参数的渐近置信区间。为了便于进一步研究,提出了一种从分布中产生随机样本的分步算法。此外,在大量样本的基础上,通过参数的均方误差和平均绝对偏差,进行了大量的模拟实验来研究参数的最大似然估计量的长期行为。实证证明了MLE的一致性。最后,通过对同一范围内的一些现有分布拟合真实数据集,展示了所提出的分布的应用。
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GDUS-Modified Topp-Leone Distribution: A New Distribution with Increasing, Decreasing, and Bathtub Hazard Functions
In this paper, we propose an extension to the Topp-Leone distribution, as introduced by [20] using the Generalized-DUS transformation given by [8]. The Topp-Leone distribution is defined on interval (0,1) and has a characteristic J-shaped frequency curve. The newly extended version of Topp-Leone distribution accommodates a variety of shapes of hazard rate functions making it a versatile distribution. We have also derived explicit expressions for some properties like ordinary moments, conditional moments, distribution of order statistics, quantiles, mean deviation, and entropy. Further, we have also discussed results on identifiability, stress-strength reliability, and stochastic ordering that are concerned with two independent random variables. For inference regarding the unknown parameters of the distribution, we derive the equations which give their maximum likelihood estimators. We also present the asymptotic confidence intervals of the unknown parameters of the distribution, based on large sample property, using the Fisher information matrix. To facilitate further studies, a step-by-step algorithm is presented to produce a random sample from the distribution. Further, extensive simulation experiments are done to study the long-term behavior of the maximum likelihood estimators of the parameters through their mean squared error and mean absolute bias on the basis of large number of samples. The consistency of the MLEs is empirically proved. Lastly, the application of the proposed distribution is shown by fitting a real-life dataset over some existing distributions in the same range.
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