对于任意边际密度的中间相关性矩阵的同时估计.
Oscar L Olvera Astivia1, Edward Kroc2, Bruno D Zumbo2
1College of Education, University of Washington, 2012 Skagit Ln, Seattle, WA, 98105, United States. oastivia@uw.edu.
Behavior research methods
|June 16, 2023
概括
这项研究引入了一种新的算法,用于模拟多变量,非正常数据. 它同时估计相关性矩阵,避免了现有的双变量方法的问题,并确保了准确的数据模拟的正确性.
科学领域:
- 社会科学 社会科学 社会科学
- 统计 统计 统计 统计
- 计算统计学 计算统计学
背景情况:
- 在社会科学中模拟多变量,非正常数据至关重要.
- 当前的方法改变了相关性结构,需要进行复杂的调整.
- 现有的技术往往以两种方式估计相关性,冒着非正确确矩阵的风险.
研究的目的:
- 介绍一种用于估计中间相关性矩阵的新算法.
- 为了解决双变量估计方法的局限性.
- 确保生成有效的 (正确的) 相关结构.
主要方法:
- 开发了一个使用随机近似的算法.
- 同时估计中间相关性矩阵的所有元素.
- 将该方法应用于模拟和实证数据集.
主要成果:
- 算法成功估计了中间的相关性矩阵.
- 在诱导所需的相关性结构方面证明了可行性.
- 避免了生成非正确确矩阵的风险.
结论:
- 提出的同时估计方法是一个可行的替代方案.
- 为模拟多变量,非正常数据提供了更强大的方法.
- 方便在统计研究中准确建模.
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