高维结构方程建模的最大概率方法
Alexander Quinter1, Xianming Tan1, Donglin Zeng1,2
1Department of Biostatistics, University of North Carolina Gillings School of Public Health, Chapel Hill, North Carolina, USA.
Statistics in medicine
|July 17, 2025
概括
我们引入了一种新的因子分析统计方法,可以处理大数据的挑战,如高维度和稀疏性. 这种方法使用最大概率理论来识别复杂数据集中的潜在因素,在COVID-19调查数据中证明了这一点.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 心理测量 心理测量 心理测量
背景情况:
- 因子分析对于大数据的维度缩小至关重要.
- 像结构方程建模 (SEM) 这样的传统方法与高维度和稀疏性作斗争.
- 在现有的因子分析技术中,确定潜在构造的数量往往没有得到解决.
研究的目的:
- 提出一种基于SEM的新型估计方法,用于因子分析.
- 解决传统方法在处理大数据特征方面的局限性.
- 将最大概率理论纳入强大的因子分析.
主要方法:
- 开发了一种新的基于SEM的估计技术.
- 使用最大概率理论进行参数估计.
- 在因子负载矩阵上强制执行稀疏性的内置方法.
主要成果:
- 拟议的方法有效地识别了独立变量和依赖变量的潜在因素.
- 实现了对因子负载矩阵的准确估计和稀疏性强制执行.
- 该方法已成功应用于COVIDiSTRESS全球调查数据集.
结论:
- 这种新方法为大数据环境中的因子分析提供了强大的工具.
- 它准确地识别了潜在的潜构造,并估计了因子负载与稀疏性.
- 在分析现实世界调查数据,例如COVID-19流行病的影响方面展示了实用的实用性.
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