对于纵向数据的混合截断的spline-local线性非参数回归模型的性能
Idhia Sriliana1,2, I Nyoman Budiantara1, Vita Ratnasari1
1Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
MethodsX
|December 13, 2024
概括
这项研究引入了一种新的混合截断斜线-局部线性非参数回归 (MTSLLNR) 模型,用于分析复杂的纵向数据. 拟议的模型在模拟和现实应用中展示了一致的发现和良好的性能.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 生物统计学 生物统计学
背景情况:
- 非参数回归模型处理复杂的数据模式.
- 混合估计器非参数回归 (MENR) 结合了多个估计器进行多变量分析.
- 纵向数据带来了独特的挑战,因为随着时间的推移重复测量.
研究的目的:
- 为纵向数据开发一种新的混合截断线-局部线性非参数回归 (MTSLLNR) 模型.
- 解决预测变量表现出不同数据模式的情况.
- 评估拟议的MTSLLNR模型的性能和一致性.
主要方法:
- 该研究提出了MTSLLNR模型,它结合了局部线性和截断的支线估计器.
- 使用一种使用两阶段估计的修改后加权最小方形 (WLS) 方法.
- 使用通用交叉验证 (GCV) 方法选择最佳节点和带宽.
主要成果:
- 进行了一项模拟研究,采用了不同的样本大小和时间点.
- 该MTSLLNR模型应用于现实世界贫困差距指数数据.
- 模拟和真实数据分析都表明了模型的一致性和有效性.
结论:
- 该MTSLLNR模型有效地模拟了带有多种预测变量模式的纵向数据.
- 该方法表现出良好的性能和一致性,通过模拟和真实数据应用来验证.
- 该MTSLLNR模型为复杂的纵向数据分析提供了可靠的方法.
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