新的两个参数混合估计器为零膨胀负二项式回归模型
Fatimah A Almulhim1, M Nagy2, Ali T Hammad3
1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|July 1, 2025
概括
这项研究引入了一种新的混合估计器,以改善在面临多线性时的零膨胀负双项回归 (ZINBR) 模型中的参数估计. 这种新的方法提高了稳定性和准确性,在复杂的数据场景中表现优于传统方法.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 生物统计学 生物统计学
背景情况:
- 零膨胀负二项式回归 (ZINBR) 模型对于过度分散和过多的零数值的计数数据至关重要.
- 多对线性对ZINBR模型中使用最大概率估计 (MLE) 的参数估计的稳定性和可靠性构成重大挑战.
研究的目的:
- 提出和评估一种新的两参混合估计器,旨在减轻ZINBR模型中的多对线性问题.
- 在高预测变量相关性条件下,提高ZINBR模型中的参数估计的精度和稳定性.
主要方法:
- 开发一种新的双参数混合估计器,将现有的偏差估计技术结合起来.
- 理论比较与已确定的偏差估计器 (里奇,,基布里亚-卢克曼,修改的里奇).
- 广泛的蒙特卡洛模拟研究,以评估在不同多线性水平下的性能,使用平均平方误差 (MSE) 和平均绝对误差 (MAE).
主要成果:
- 拟议的混合估计器与传统偏差估计器相比,表现优越,特别是在具有高多对线性情景中.
- 模拟结果表明混合估计器的MSE和MAE较低,这意味着准确性和稳定性得到改善.
- 现实世界的数据应用证实了估计器在产生可靠的参数估计中的有效性.
结论:
- 新的双参数混合估计器代表了ZINBR模型中参数估计的重大进步.
- 该估计器对于具有多对线性特征的复杂数据集特别有益.
- 这些发现支持采用这种混合估计器,以便对计数数据进行更强大的统计建模.
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