时间序列网络模型的动态合适指数截止值
Siwei Liu1, Christopher M Crawford2, Zachary F Fisher2
1Human Ecology, University of California, Davis, Davis, CA, USA.
Multivariate behavioral research
|October 1, 2025
概括
本研究将动态适合指数 (DFI) 适应时间序列分析,为网络模型提供量身定制的切断值. 新的方法,DFI_A和DFI_B,可以更好地检测模型的错误规格,特别是在小样本大小的情况下.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 网络分析 网络分析
背景情况:
- 动态合适指数 (DFI) 是一种基于模拟的方法,用于确定模型合适指数的截止值.
- 现有的方法可能无法充分检测时间序列网络模型中的模型错误规范.
研究的目的:
- 将动态合适指数 (DFI) 扩展到时间序列分析.
- 开发改进的方法来导出适合指数切断值,以检测时间序列网络模型中遗漏的路径.
- 原始DFI的地址限制具有小效果或样本大小.
主要方法:
- 模拟研究用于评估时间序列网络模型的DFI截止值.
- 与已建立的基准标准 (Hu & Bentler) 进行DFI截止值的比较.
- 使用宽松标准开发和评估两种替代的DFI方法 (DFI_A和DFI_B).
主要成果:
- 在时间序列网络中检测遗漏路径的DFI切线比传统基准更接近精确匹配.
- 截止值受到变量数,网络密度,时间点和错误规范类型的影响.
- 原始DFI未能在小效应或样本大小下以严格的错误率限制识别遗漏路径的切断值.
- DFI_A和DFI_B提供了可行的替代方案,可以根据更宽松的标准来推导切断值.
结论:
- 扩展的DFI为时间序列网络模型提供了更准确的适合指数截止值.
- DFI_A和DFI_B提供了用于检测模型错误规范的实际解决方案,特别是在具有挑战性的数据条件下.
- 这些方法提高了模型评估在时间序列网络分析的可靠性.
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