模拟多变量纵向数据共变矩阵的Cholesky因子
Priya Kohli1, Tanya P Garcia2, Mohsen Pourahmadi3
1Department of Mathematics, Connecticut College, 270 Mohegan Avenue, New London, CT 06320, United States.
概括
这项研究引入了一种用于在多变量纵向数据中建模共变量的新方法. 它通过设计确保了正确的确定性,使协差模型对复杂的数据集更加实用.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
背景情况:
- 在多变量纵向数据中模拟共变性是复杂的,因为相互响应的相关性.
- 现有的方法,如修改的巧尔斯基分解,在确保创新协差矩阵的正确性方面面临挑战.
研究的目的:
- 开发一种可靠的方法,用于在多变量纵向数据中建模共变量矩阵.
- 用线性协差模型来解决协差建模中的正定性挑战.
主要方法:
- 使用带有基数矩阵和非负标数系数的线性协差模型的一个子类.
- 通过构建来确保正确的确定性,克服以前方法的局限性.
- 采用代的大化-最小化算法来进行最大概率估计.
主要成果:
- 拟议的方法保证了模拟的协差矩阵的正确性.
- 最大概率估计器被证明是非对称的正常和一致.
- 模拟和数据示例证明了该方法的有效性.
结论:
- 这种新的方法增强了线性协差模型的现实性和实用性.
- 该方法为多变量纵向数据的协差结构提供了有效的模型.
- 该技术为复杂的统计建模挑战提供了可行的解决方案.
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