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电力林德利分布的新型扩展,具有统计属性和适用于COVID-19数据的应用
Danish Qayoom1, Aafaq A Rather2, Eid Sadun Alotaibi3
1Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune, India.
Scientific reports
|August 19, 2025
概括
一个新的指数奇数罗马克斯Power Lindley分布增强了医疗数据分析,特别是COVID-19数据. 与现有的统计分布相比,这种新型模型显示出更高的适应性和准确性.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 医学统计 医学统计
背景情况:
- 在医疗和健康数据分析中,对高级概率分布的需求日益增加.
- 模拟复杂的健康相关数据集的挑战,包括COVID-19等传染病.
研究的目的:
- 介绍一个新的概率分布:指数式奇数罗马克斯功率林德利 (EOLPD) 分布.
- 研究EOLPD的理论特性和统计特征.
- 展示EOLPD在模拟现实世界医疗数据,特别是COVID-19数据集中的实用性.
主要方法:
- 使用T-X转换技术构建EOLPD,在Lomax家族中嵌入Power Lindley分布.
- 导出关键的统计性质:概率和累积分布函数,可靠性函数,时刻,时刻生成函数,顺序统计和.
- 使用多种方法进行参数估计,通过模拟研究验证.
- 使用COVID-19数据对已建立分布进行比较性能分析.
主要成果:
- 为EOLPD建立了全面的理论框架.
- 模拟研究证实了参数估计方法的准确性.
- 与现有模型相比,EOLPD在COVID-19数据集上展示了卓越的适应性和建模精度.
结论:
- 显数奇数罗马克斯功率林德利分布是医学数据分析的一个有价值的新工具.
- EOLPD在建模复杂的健康数据方面提供了增强的性能,包括与流行病相关的信息.
- 这种新型分布为医疗保健中的统计分析提供了更准确和更适应的方法.
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