在纵向研究中评估缺失的数据分析技术:传统和机器学习方法
1Department of Psychology, University of Virginia.
Psychological methods
|October 30, 2025
概括
完全信息最大概率 (FIML) 在增长曲线模型中最有效用于缺失非随机 (MNAR) 数据. 两阶段的稳定估计 (TSRE) 很好地处理随机丢失 (MAR) 数据,而机器学习方法显示的好处有限.
科学领域:
- 统计建模 统计建模
- 纵向数据分析的数据分析.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 处理非正常和缺失非随机 (MNAR) 数据对于传统方法 (如全信息最大概率 (FIML)) 是复杂的,因为正常分布假设.
- 双阶段稳定估计 (TSRE) 可以处理非正常数据,但其在带有MNAR条件的纵向研究中的表现较少被探索.
- 机器学习 (ML) 提供了一个替代方案,不需要分布假设,并显示MNAR数据的前景,尽管其在随机失踪 (MAR) 和MNAR的纵向研究中的使用尚未得到充分探索.
研究的目的:
- 为了比较六种分析技术在增长曲线建模中缺少数据的有效性.
- 评估样本大小,缺失数据率,机制和分布对模型估计准确性和效率的影响.
- 评估传统 (FIML,TSRE) 和机器学习 (K-最近邻居,missForest,micecart,miceForest) 的归算方法.
主要方法:
- 使用蒙特卡洛模拟来评估分析技术.
- 使用增长曲线建模框架来评估缺失的数据处理.
- 六种技术进行了比较:FIML,TSRE,K-最近邻居,missForest,小鼠车和小鼠森林.
主要成果:
- 完全信息最大概率 (FIML) 证明了对遗漏的非随机 (MNAR) 数据的最高有效性.
- 两个阶段的稳定估计 (TSRE) 在缺失随机 (MAR) 数据方面表现最好.
- 只有在特定条件下,MissForest才能显示出优势:分布高度偏差,样本大小 (n ≥1,000),缺失数据率低.
结论:
- 对于具有MNAR数据的纵向生长曲线模型,建议使用FIML.
- 对于MAR数据场景来说,TSRE是一个合适的选择.
- 像missForest这样的机器学习归算方法具有利基应用,但需要仔细考虑数据特征和样本大小.
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