在使用Glasso和Atan调节的网络分析中,通过EM和多重推算处理丢失的数据
Kai Jannik Nehler1, Martin Schultze1
1Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany.
Multivariate behavioral research
|May 26, 2025
概括
这项研究比较了多重归算和EM方法来处理心理网络中缺失的数据. 堆叠的多重归因对于非凸规律化最为一致,而双步EM在凸规律化方面表现出色.
科学领域:
- 心理学网络分析 网络分析
- 统计方法学的统计方法.
- 缺失数据处理 缺失数据处理
背景情况:
- 目前关于心理网络中缺少数据的文献仅限于基于概率的方法.
- 现有的方法往往侧重于凸的规范化,各种缺失的数据处理实现.
- 需要对不同缺失数据处理技术进行标准化和比较评估.
研究的目的:
- 实施和评估一个缺失的数据处理方法,使用堆叠的多重归算.
- 为了比较堆叠的多重归算与直接和两步EM方法.
- 在不同的网络条件和规范化类型下评估性能.
主要方法:
- 模拟的横截面心理网络,具有不同的网络大小,观察和缺失.
- 实现堆叠多重归算,直接EM和两步EM方法.
- 使用凸 (glasso) 和非凸 (atan) 调整与EBIC和BIC模型选择进行评估.
主要成果:
- 缺失数据处理方法在许多模拟条件中显示了类似的性能.
- 使用glasso和EBIC的两步EM在整体上表现最好,紧随其后的是堆叠的多重归因.
- 堆叠的多重归算是与BIC. atan规范化最一致的.
结论:
- 堆叠的多重归算为心理网络中缺失的数据处理提供了可行和一致的替代方案,特别是在非凸规则化的情况下.
- 缺失数据处理方法的选择可能取决于所采用的规范化技术.
- 需要进行进一步的研究,比较网络分析中的归算和EM方法.
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