在Poisson回归模型中克服异常值和多对线性新的强大的两参数估计器
Hebatalla H Mohammad1, Ali T Hammad2, Abeer A El-Helbawy3
1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|July 28, 2025
概括
本研究引入了一种新的强大的Poisson两参数估计器 (PMT-PTE),以解决Poisson回归模型中异常值和多对线性问题的问题. 在模拟和现实数据分析中,PMT-PTE表现出卓越的性能.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 数据科学数据科学数据科学
背景情况:
- 普森最大概率估计器 (PMLE) 对异常值和多对线性敏感.
- 现有的可靠和有偏见的估计器分别解决这些问题,而不是同时解决.
- 存在对Poisson回归中的异常值和多对线性都可靠的估计器的需求.
研究的目的:
- 提出一种新的,强大的Poisson两个参数估计器 (PMT-PTE).
- 在Poisson回归模型中同时处理异常值和多对线性.
- 评估拟议的PMT-PTE与现有估计器的性能.
主要方法:
- 通过结合转换的M估计器 (MT) 和两个参数的估计,开发了Poisson两参数估计器 (PMT-PTE).
- 拟议估计器与现有方法的理论比较.
- 蒙特卡洛模拟用于评估各种异常值和多对线性情景下的表现.
主要成果:
- 拟议的PMT-PTE估计器在异常值和多对线性情景中显著优于现有的估计器.
- 模拟结果证实了PMT-PTE的稳定性和效率.
- 对现实世界数据集的分析验证了理论和模拟发现.
结论:
- 在处理异常值和多对线性时,PMT-PTE提供了Poisson回归分析的优越方法.
- 拟议的估计器为可靠的统计建模提供了一个有价值的新工具.
- 这项工作推进了处理Poisson回归中常见数据挑战的方法.
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