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经过修改的基斯·托普-莱昂模型的不同估计方法与应用程序和定量回归
Safar M Alghamdi1, Olayan Albalawi2, Sanaa Mohammed Almarzouki3
1Department of Mathematics and Statistics, College of Science, Taif University, Taif, Saudi Arabia.
PloS one
|September 13, 2024
概括
一个新的修改后的Kies Topp-Leone (MKTL) 分布有效地在单位间隔内建模数据. 模拟研究证实了最大概率方法在参数估计方面优越,突出了MKTL.
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:
- 单元区间 (0,1) 或[0,1]上的数据需要特殊的分布.
- 现有的分布可能无法捕捉这些数据的多样性特征.
- 基斯-托普-莱昂分布是已知的间隔边界数据模型.
研究的目的:
- 介绍了新的修改过的基斯-托普-莱昂 (MKTL) 分布.
- 评估MKTL各种参数估计方法的性能.
- 展示MKTL分布在现实世界数据建模中的实用性和优势.
主要方法:
- 修改后的基斯-托普-莱昂分布 (MKTL) 的发展.
- 应用十二种不同的参数估计技术.
- 蒙特卡洛模拟实验用于比较估计方法.
- 使用三个真实世界数据集进行验证,并与竞争模型进行比较.
主要成果:
- MKTL分布呈现出理想的密度和危险率函数形状.
- 最大概率估计成为最有效的方法.
- 与其他模型相比,MKTL分布表现出优越的性能.
- 基于MKTL开发的量子回归模型提供了具有竞争力的适合性.
结论:
- 修改后的Kies Topp-Leone (MKTL) 分布是一种灵活而有效的工具,用于建模单位间隔数据.
- 建议对MKTL参数进行最大概率估计.
- 对于以间隔为界的数据分析,MKTL比现有的分布提供了显著的进步.
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