在函数对函数的线性定量回归上
Muge Mutis1, Ufuk Beyaztas2, Filiz Karaman1
1Department of Statistics, Yildiz Technical University, Esenler-Istanbul, Turkiye.
Journal of applied statistics
|March 5, 2025
概括
我们开发了新的功能部分量子位回归算法,用于准确的函数对函数线性量子位回归. 这些方法有效估计系数函数,在模拟和现实数据分析中表现优于现有技术.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 功能数据分析 功能数据分析
背景情况:
- 功能数据分析 (FDA) 涉及观察是函数的数据.
- 函数对函数回归模型是函数之间的关系.
- 量子位回归通过估计条件量子位提供了比平均回归更全面的分析.
研究的目的:
- 介绍两个用于函数部分定量回归的新算法.
- 在函数对函数线性定量回归模型中准确有效地估计回归系数函数.
- 为了应对函数回归中的无限维数据的挑战.
主要方法:
- 功能性部分定量回归分解来减少维度.
- 对于近似部分量子力回归元件的基础扩展.
- 无限维模型的近似使用多变量量子力回归.
主要成果:
- 拟议的算法在有限样本场景中表现出卓越的性能.
- 经验结果显示,与现有方法相比,准确性和效率有所提高.
- 在R包中成功实现 ffpqr.
结论:
- 开发的算法为函数部分定量回归提供了一种有效的方法.
- 这些方法为分析复杂的功能数据提供了有价值的工具.
- ffpqr套件有助于应用这些先进的统计技术.
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