在网络分析中的缺失数据处理的基于模拟的性能评估
Kai Jannik Nehler1, Martin Schultze1
1Department of Psychology, Goethe Universität Frankfurt.
Multivariate behavioral research
|January 21, 2024
概括
这项研究评估了用于心理构造的网络分析中处理缺失数据的方法. 直接EM算法通常优于其他方法,特别是在大样本大小或小网络的情况下.
科学领域:
- 心理学 心理学 心理学
- 网络科学 网络科学
- 统计 统计 统计 统计
背景情况:
- 网络分析越来越多地用于心理构造.
- 应用研究人员缺乏明确的指导方针来处理网络分析中缺失的数据.
研究的目的:
- 在网络分析中比较不同缺失数据处理技术的性能.
- 在各种条件下确定恢复人口网络的最佳方法.
主要方法:
- 模拟研究比较了两步EM算法,直接EM算法和双向删除.
- 调查了不同的网络大小,样本大小,缺失数据机制和缺失值的百分比.
- 基于精度矩阵损失,边缘集识别和网络统计数据的评估网络恢复.
主要成果:
- 只有大样本大小或小网络 (p=10) 才能观察到足够的网络恢复.
- 直接电磁算法在大多数条件下都表现出卓越的灵敏度和性能.
- 两步EM算法在非常大的n/p比率下显示出更好的特异性.
- 偶尔删除经常无法收,并产生了糟糕的结果.
结论:
- 对于大多数缺少数据的网络分析应用程序,建议使用直接EM算法.
- 直接EM算法有效地减轻了缺失数据的影响.
- 对两步EM算法的进一步修改可能会提高其性能.
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