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没有数据的规范化横截面网络建模:方法比较

Carl F Falk¹, Joshua Starr²

¹Department of Psychology, McGill University, Montreal, Canada.

Multivariate behavioral research

|September 17, 2025

概括

本研究比较了使用图形拉索 (glasso) 在网络建模中处理缺失数据的方法. 预期最大化算法与交叉验证证明了心理网络分析的最佳性能.

科学领域:

网络科学网络科学
心理测量心理测量
统计建模统计建模

背景情况:

网络建模对于分析心理变量至关重要,通常使用规范化的高斯图形模型 (GGM) 与图形拉索 (glasso).
现有的处理失踪数据的方法是不发达的,这限制了有效的数据收集设计的使用.
计划的缺失数据设计可以减少参与者的负担,但需要强大的缺失数据处理技术.

研究的目的:

为了比较在图形拉索框架内处理缺失数据的三个不同的方法.
在不同的模拟条件下评估这些方法的性能,包括样本大小和缺失数据比例.
为分析缺乏观测的心理网络数据的研究人员提供实际指导.

主要方法:

在glasso之前使用和共变矩阵的两阶段估计方法.
单阶段方法将glasso与预期最大化 (EM) 算法结合起来,使用EBIC或交叉验证来调整参数选择.
一项模拟研究,评估不同样本大小,缺失数据比例和网络结构的性能,并补充了现实数据示例.

主要成果:

预期最大化 (EM) 算法与调整参数选择的交叉验证相结合,在评估的方法中表现最佳.
这三种比较方法都显示出可行性,特别是在样本规模较大,缺失数据比例较低的场景中.
该研究确定了在心理网络分析中选择适当的缺失数据处理技术的实际考虑因素.

结论:

具有交叉验证的EM算法提供了一个有前途的策略,用于解决图形激光网络分析中缺失的数据.
研究人员在选择心理网络分析方法时应考虑样本大小和缺失数据的流行率.
进一步的方法开发是有必要的,以提高复杂的网络模型中缺少数据的处理.

关键词:

在EM算法中,EM算法网络建模网络建模协变性结构建模的模型.高斯的图形模型.拉索 (Lasso) 是一个拉索.缺失的数据缺失的数据患者报告的结果.结构方程建模结构方程建模两个阶段的估计估计.

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