Jung Kwanghee, Takane Yoshio, Hwang Heungsun, Woodward Todd S
Department of Pediatrics, Children's Learning Institute, The University of Texas Health Science Center at Houston, 7000 Fannin UCT 2373J, Houston, TX, 77030 , USA.
University of Victoria, Victoria, Canada.
Psychometrika. 2016 Jun;81(2):565-81. doi: 10.1007/s11336-015-9440-6. Epub 2015 Feb 20.
We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.
我们扩展了动态广义结构成分分析（GSCA），以增强其在多主体时间序列数据结构方程建模中的数据分析能力。多个主体的时间序列数据通常具有层次结构，其中时间点嵌套在主体内，而主体又嵌套在一个组内。所提出的方法称为多级动态GSCA，它适应时间序列数据中的嵌套结构。通过明确考虑嵌套结构，该方法允许通过查看相应随机效应的方差估计来研究载荷和路径系数的主体间变异性，以及观测变量和潜在变量之间的固定载荷和潜在变量之间的固定路径系数。我们通过将该方法应用于多主体功能神经成像数据进行脑连接性分析来证明所提出方法的有效性，其中时间序列数据级别的测量嵌套在主体内。
