使用链图形VAR模型进行分组
Jonathan J Park1, Sy-Miin Chow1, Sacha Epskamp2
1Department of Human Development and Family Studies, The Pennsylvania State University.
Multivariate behavioral research
|February 14, 2024
概括
一个新的奇特模型,分组链图形向量自回归 (scGVAR),识别了具有共享动态网络结构的子组. 它在网络分析中提供了更好的灵敏度来检测细微的群体差异.
科学领域:
- 统计 统计 统计 统计
- 网络分析 网络分析
- 心理测量 心理测量 心理测量
背景情况:
- 通过将个人内部数据汇集在一起,Idio-thetic方法可以将名义学和异形学推理结合起来.
- 现有的方法在分组内识别动态网络结构时面临挑战.
研究的目的:
- 介绍一个新的奇特的模型,分组链图形向量自回归 (scGVAR).
- 能够识别具有共同的动态网络结构的子组,无论是滞后的还是同时发生的.
主要方法:
- 开发了分组链图形向量自回归 (scGVAR) 模型.
- 进行蒙特卡洛模拟,将scGVAR与交替最小平方VAR (ALS VAR) 进行比较.
主要成果:
- 当个人在当代动态中不同时,scGVAR表现出了相似方法的承诺.
- scGVAR在检测微妙的群体差异方面显示出更高的灵敏度,I型错误率较低.
- 当群体差异很大时,ALS VAR表现良好.
结论:
- scGVAR是识别具有共同动态网络结构的子组的一个有价值的工具.
- 该研究强调了scGVAR和ALS VAR在现实应用中的优点和局限性.
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