基於樹的階層混合效果模型中的修改樹基選擇:模擬研究和實際數據應用
Asrirawan1,2, Khairil Anwar Notodiputro2, Budi Susetyo2
1Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sulawesi Barat, Indonesia.
MethodsX
|April 28, 2025
概括
两个新的统计模型,3Trees-EvTree和3Trees-CTree,通过减少偏差和提高预测准确性,改善了对现有3Trees方法的等级混合效应建模.
科学领域:
- 统计学学习 统计学学习
- 混合效果建模模的模拟
- 计算统计学 计算统计学
背景情况:
- 层次混合效应模型 (3Trees) 使用分类和回归树 (CART),但遭受贪的算法导致过拟合和偏见的分裂.
- 现有的3Trees方法可能是低于最佳的,这会影响统计学学习中的整体模型性能.
- 目前3Trees方法的局限性要求开发更强大,更准确的方法.
研究的目的:
- 引入两种新的方法,3Trees-EvTree和3Trees-CTree,旨在克服现有的3Trees模型的局限性.
- 为了提高预测准确度和减少层次混合效应建模中的偏差.
- 通过模拟和现实世界的数据,对拟议的方法与既定技术的性能进行评估.
主要方法:
- 开发了两个新的算法:3Trees-EvTree和3Trees-CTree,基于3Trees框架.
- 使用分类和回归树 (CART) 与改进的算法来缓解过拟合和分割选择偏差.
- 使用平均平方误差 (MSE),集群MSE (ClusMSE),预测MSE (PMSE),集群PMSE (ClusPMSE) 和偏差标准进行性能评估.
主要成果:
- 与以前的方法相比,3Trees-EvTree方法显示出更高的参数估计和预测准确性,特别是在clusMSE和clusPMSE指标下.
- 3Trees-CTree模型在低相关性设置和半线性函数中表现出强的性能.
- 两种拟议的方法都在现实数据集应用中证实了它们在竞争方法上的优势,包括家庭支出估计.
结论:
- 新的3Trees-EvTree和3Trees-CTree模型有效地解决了在层次混合效果建模中传统3Trees方法的局限性.
- 这些先进的方法提供了更好的预测准确性和减少偏差,导致更可靠的统计推理.
- 这些发现表明,3Trees-EvTree和3Trees-CTree代表了统计学习和混合效应建模应用的重大进步.
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