A New Odd Beta Prime-Burr X Distribution with Applications to Petroleum Rock Sample Data and COVID-19 Mortality Rate

IF 2.2 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Data Pub Date : 2023-09-19 DOI:10.3390/data8090143
Ahmad Abubakar Suleiman, Hanita Daud, Narinderjit Singh Sawaran Singh, Aliyu Ismail Ishaq, Mahmod Othman
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引用次数: 0

Abstract

In this article, we pioneer a new Burr X distribution using the odd beta prime generalized (OBP-G) family of distributions called the OBP-Burr X (OBPBX) distribution. The density function of this model is symmetric, left-skewed, right-skewed, and reversed-J, while the hazard function is monotonically increasing, decreasing, bathtub, and N-shaped, making it suitable for modeling skewed data and failure rates. Various statistical properties of the new model are obtained, such as moments, moment-generating function, entropies, quantile function, and limit behavior. The maximum-likelihood-estimation procedure is utilized to determine the parameters of the model. A Monte Carlo simulation study is implemented to ascertain the efficiency of maximum-likelihood estimators. The findings demonstrate the empirical application and flexibility of the OBPBX distribution, as showcased through its analysis of petroleum rock samples and COVID-19 mortality data, along with its superior performance compared to well-known extended versions of the Burr X distribution. We anticipate that the new distribution will attract a wider readership and provide a vital tool for modeling various phenomena in different domains.
一种新的奇β Prime-Burr X分布及其在石油样品数据和COVID-19死亡率中的应用
在本文中,我们使用奇数素数广义(OBP-G)分布族,即OBP-Burr X (OBPBX)分布,开创了一个新的Burr X分布。该模型的密度函数为对称型、左偏型、右偏型和倒j型,而风险函数为单调递增型、递减型、浴盆型和n型,适合于偏态数据和故障率的建模。得到了新模型的各种统计性质,如矩、矩生成函数、熵、分位数函数和极限行为。利用最大似然估计方法确定模型的参数。通过蒙特卡罗仿真研究,验证了极大似然估计的有效性。通过对石油岩石样本和COVID-19死亡率数据的分析,这些发现证明了OBPBX分布的经验应用和灵活性,以及与众所周知的扩展版本Burr X分布相比的优越性能。我们预计新的发行版将吸引更广泛的读者,并为不同领域的各种现象建模提供一个重要的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Data
Data Decision Sciences-Information Systems and Management
CiteScore
4.30
自引率
3.80%
发文量
0
审稿时长
10 weeks
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