在多个指标多个原因模型中的贝叶斯规范化
1Graduate School of Education, Stanford University.
Psychological methods
|July 27, 2023
概括
贝叶斯规范化方法增强结构方程建模,特别是在小样本大小的情况下. 马和尖和石先提供了卓越的参数准确性和对共变量效应的预测.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 机器学习 机器学习
背景情况:
- 正规化方法越来越多地被整合到结构方程建模 (SEM) 中,以增强变量选择,模型估计和预测.
- 贝叶斯式方法提供了一个灵活的框架,用于将规范化先验纳入SEM.
研究的目的:
- 比较各种贝叶斯规范化方法来分析多指标多原因 (MIMIC) 模型中的共变效应.
- 评估超参数设置对处罚前表现的影响.
- 使用交叉验证评估预测的准确性.
主要方法:
- 进行了一项模拟研究,以比较贝叶斯规范化方法,包括脊,拉索,自适应拉索,尖和板前 (SSP) 和马前.
- 这些方法应用于MIMIC模型,以估计稀疏结构系数矩阵.
- 对超参数设置进行了敏感性分析,并通过交叉验证评估了预测准确性.
主要成果:
- 惩罚先验在小样本大小和对直线共变量中表现优于扩散先验.
- 全球惩罚先 (ridge,lasso) 显示了更高的趋同率和力量.
- 当地和全球惩罚先验 (马,SSP) 提供了更准确的参数估计和改进的因子得分预测,导致节的模型.
结论:
- 贝叶斯规范化,特别是马和SSP先验,对于变量选择和SEM中准确的参数估计是有效的.
- 惩罚先验对于处理具有挑战性的数据条件,如小样本大小和多对线性至关重要.
- 这些方法提高了MIMIC模型中的预测准确性和模型储蓄性.
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