DUS Topp-Leone-G Family of Distributions: Baseline Extension, Properties, Estimation, Simulation and Useful Applications.

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2024-11-13 DOI:10.3390/e26110973
Divine-Favour N Ekemezie, Kizito E Anyiam, Mohammed Kayid, Oluwafemi Samson Balogun, Okechukwu J Obulezi
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引用次数: 0

Abstract

This study introduces the DUS Topp-Leone family of distributions, a novel extension of the Topp-Leone distribution enhanced by the DUS transformer. We derive the cumulative distribution function (CDF) and probability density function (PDF), demonstrating the distribution's flexibility in modeling various lifetime phenomena. The DUS-TL exponential distribution was studied as a sub-model, with analytical and graphical evidence revealing that it exhibits a unique unimodal shape, along with fat-tail characteristics, making it suitable for time-to-event data analysis. We evaluate parameter estimation methods, revealing that non-Bayesian approaches, particularly Maximum Likelihood and Least Squares, outperform Bayesian techniques in terms of bias and root mean square error. Additionally, the distribution effectively models datasets with varying skewness and kurtosis values, as illustrated by its application to total factor productivity data across African countries and the mortality rate of people who injected drugs. Overall, the DUS Topp-Leone family represents a significant advancement in statistical modeling, offering robust tools for researchers in diverse fields.

DUS Topp-Leone-G 分布系列:基线扩展、特性、估算、模拟和实用应用
本研究介绍了 DUS Topp-Leone 分布系列,这是由 DUS 变换器增强的 Topp-Leone 分布的新扩展。我们推导了累积分布函数(CDF)和概率密度函数(PDF),证明了该分布在模拟各种寿命现象时的灵活性。我们将 DUS-TL 指数分布作为一个子模型进行了研究,分析和图形证据表明,它呈现出独特的单峰形状,并具有胖尾特征,因此适用于时间到事件数据分析。我们对参数估计方法进行了评估,发现非贝叶斯方法,特别是最大似然法和最小二乘法,在偏差和均方根误差方面优于贝叶斯技术。此外,该分布还能有效地对具有不同偏度和峰度值的数据集进行建模,其在非洲国家全要素生产率数据和注射毒品者死亡率中的应用就说明了这一点。总之,DUS Topp-Leone 系列代表了统计建模领域的一大进步,为不同领域的研究人员提供了强大的工具。
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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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