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Updated: Feb 19, 2026

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用一般化的马歇尔-奥尔金-库马拉斯瓦米分布建模物理数据

Selim Gündüz¹, Egemen Ozkan², Kadir Karakaya³

¹Department of Business Administration, Faculty of Business, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.

|February 17, 2026

概括

引入了一个新的局限数据统计分布,为各种危险率提供灵活的建模. 它的参数使用多种方法进行估计,在医学,政治,物理和教育领域的现实应用中表现出强的性能.

科学领域:

统计统计统计统计
可能性分布的概率分布.
数学建模的数学建模

背景情况:

传统的统计模型往往在有限的数据上扎.
需要灵活的分布来捕捉不同的危险率形状.
像Beta和Kumaraswamy这样的现有分布可能并不总是最佳的.

研究的目的:

引入一个新的统计分布,定义在一个有界的间隔.
检查新分布的属性,包括时刻和相关曲线.
开发和评估参数估计技术和量子回归模型.

主要方法:

引入了一个新的有限概率分布.
调查的时刻,洛伦茨曲线和邦费罗尼曲线.
使用最大概率,最小平方,安德森-达林,克拉梅尔-·米塞斯和间隔方法进行参数估计.
进行蒙特卡洛模拟以评估估计性能.
开发了局限依赖变量的一种定量回归模型.

主要成果:

拟议的分布有效地模拟了各种危险率形状 (例如,反向浴,浴,增加,减少).
评估了参数估计方法,并使用模拟指导性能评估.
新的分布证明了在医学,政治,物理和教育领域的真实世界数据中的适用性和灵活性.
在特定的局限数据建模场景中表现优于Beta和Kumaraswamy分布.

结论:

新型分布提供了一个强大而灵活的工具来分析有限的数据.
开发的量子回归模型增强了模拟有限依赖变量的能力.
分布是一种可行的替代现有模型,在科学和教育领域具有广泛的适用性.

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