通过泰勒的M估计器对大维形状矩阵的测试
Runze Li1, Weiming Li2, Qinwen Wang3
1Department of Statistics, Pennsylvania State University, University Park, PA 16802-2111, USA.
Journal of the American Statistical Association
|June 9, 2025
概括
这项研究为高维数据引入了强大的统计测试,扩展了泰勒的M估计器用于形状矩阵. 新方法在各种分布中表现良好,为复杂的数据集提供了强大的替代方案.
科学领域:
- 统计 统计 统计 统计
- 多变量分析多变量分析
- 强大的统计数据.
背景情况:
- 经典的泰勒的M估计理论仅限于低维数据和圆群体.
- 在一般人群中,对于泰勒的M估计器的高维统计推理尚未得到充分证实.
研究的目的:
- 在大维环境中开发形状矩阵的同一性和平等性的假设测试.
- 将泰勒的M估计器的适用性扩展到维度
p 接近样本大小n 的场景. - 为一般人群提供一个统一的光谱理论,用于大维的泰勒M估计器.
主要方法:
- 使用泰勒的M估计器的固有值.
- 开发对形状矩阵的同一性和平等性的测试.
- 建立一个统一的理论,用于大维泰勒的M估计器的光谱.
主要成果:
- 拟议的测试在一个大维框架中是有效的 (p / n 汇聚到 (0,1) 中的极限).
- 测试适用于广泛的多变量分布,包括圆分布.
- 模拟研究证实了开发的测试的良好性能和稳定性.
结论:
- 该研究成功地将泰勒的M估计器和假设测试扩展到高维设置.
- 开发的方法为传统的协差矩阵分析提供了可靠的替代方案.
- 经验分析证明了投资组合分析的实际实用性.
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