Arctan功率分布:属性、分位数和模态回归在生物医学数据中的应用

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Mathematical & Computational Applications Pub Date : 2023-02-14 DOI:10.3390/mca28010025
Suleman Nasiru, A. Abubakari, C. Chesneau
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引用次数: 0

摘要

(概率)分布在生物医学领域的作用不可低估。因此,在这个领域中使用了几个分布来进行统计分析和推断。在本研究中,我们开发了arctan功率(AP)分布,并利用生物医学数据说明其应用。这种分布是灵活的,因为它的概率密度函数表现出左偏、右偏、J形和倒J形等特征。相应的风险率函数的特征也表明该分布能够对具有单调和非单调故障率的数据进行建模。还创建了AP分布的双变量扩展,以模拟两个随机变量或数据对之间的相互依赖关系。应用表明,AP分布比其他现有分布更适合生物医学数据。使用贝叶斯方法也可以相当准确地估计分布的参数,并对其进行了阐述。最后,基于AP分布的分位数和模态回归模型比现有的回归模型更适合生物医学数据。
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The Arctan Power Distribution: Properties, Quantile and Modal Regressions with Applications to Biomedical Data
The usefulness of (probability) distributions in the field of biomedical science cannot be underestimated. Hence, several distributions have been used in this field to perform statistical analyses and make inferences. In this study, we develop the arctan power (AP) distribution and illustrate its application using biomedical data. The distribution is flexible in the sense that its probability density function exhibits characteristics such as left-skewedness, right-skewedness, and J and reversed-J shapes. The characteristic of the corresponding hazard rate function also suggests that the distribution is capable of modeling data with monotonic and non-monotonic failure rates. A bivariate extension of the AP distribution is also created to model the interdependence of two random variables or pairs of data. The application reveals that the AP distribution provides a better fit to the biomedical data than other existing distributions. The parameters of the distribution can also be fairly accurately estimated using a Bayesian approach, which is also elaborated. To end the study, the quantile and modal regression models based on the AP distribution provided better fits to the biomedical data than other existing regression models.
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来源期刊
Mathematical & Computational Applications
Mathematical & Computational Applications MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
自引率
10.50%
发文量
86
审稿时长
12 weeks
期刊介绍: Mathematical and Computational Applications (MCA) is devoted to original research in the field of engineering, natural sciences or social sciences where mathematical and/or computational techniques are necessary for solving specific problems. The aim of the journal is to provide a medium by which a wide range of experience can be exchanged among researchers from diverse fields such as engineering (electrical, mechanical, civil, industrial, aeronautical, nuclear etc.), natural sciences (physics, mathematics, chemistry, biology etc.) or social sciences (administrative sciences, economics, political sciences etc.). The papers may be theoretical where mathematics is used in a nontrivial way or computational or combination of both. Each paper submitted will be reviewed and only papers of highest quality that contain original ideas and research will be published. Papers containing only experimental techniques and abstract mathematics without any sign of application are discouraged.
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