通过弱条件期望的随机对象的非线性全球回归
Satarupa Bhattacharjee1, Bing Li2, Lingzhou Xue2
1Department of Statistics, University of Florida.
概括
这项研究为复杂的对象值数据引入了一个新的非线性Fréchet回归模型. 该方法扩展了现有的技术,为分析各种非欧几里德数据集提供了强大的框架.
科学领域:
- 统计数据
- 机器学习
- 数据科学
背景情况:
- 来自度量空间的对象值数据越来越常见.
- 现有的回归模型与复杂的非欧几里德式预测和响应变量作斗争.
- 缺少对象值回归的一般框架.
研究的目的:
- 开发对象值数据的一般非线性回归框架.
- 使用卡尔曼运算符引入一个弱条件Fréchet平均值.
- 将回归分析扩展到复杂的非欧几里德预测和响应空间.
主要方法:
- 使用复制内核希尔伯特空间 (RKHS) 嵌入用于非线性建模.
- 通过卡尔曼运算符定义一个弱条件Fréchet平均值.
- 建立条件和弱条件之间的关系Fréchet意味着.
主要成果:
- 提出了一个全新的非线性Fréchet回归模型.
- 这种新模型包含了如线性内核Fréchet回归等现有方法.
- 估计的理论属性是使用尺度空间的内在几何学分析的.
结论:
- 提出的方法为分析复杂的对象值数据提供了强大的工具.
- 该框架是多用途的,适用于各种非欧几里德数据类型.
- 数字研究证实了该方法在现实应用中的有效性.
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