在扩展多个指标多个原因模型中对变量选择的规范变量贝叶斯近似方法
1University of Hong Kong.
Multivariate behavioral research
|April 10, 2025
概括
本研究引入了一种新的变量贝叶斯期望最大化算法 (VBEM),用于结构方程建模中的高效变量选择,平衡心理研究中的预测准确性和节性.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 社会科学 社会科学 社会科学
背景情况:
- 变量选择在结构方程建模中至关重要,用于平衡预测准确性和节性.
- 现有的贝叶斯规范化方法用于稀疏性是计算密集的.
- 马尔科夫链蒙特卡洛 (MCMC) 技术限制了当前方法的实际实用性.
研究的目的:
- 为变量选择提出一个计算效率高的变量贝叶斯期望最大化算法 (VBEM).
- 扩展多个指标多个原因 (MIMIC) 模型,以增强变量选择能力.
- 引入一个部分确认框架,以灵活地纳入先前知识和规范化.
主要方法:
- 开发了一个变化的贝叶斯期望最大化 (VBEM) 算法.
- 扩展了多个指标多个原因 (MIMIC) 模型.
- 在探索-确认连续中实施了部分确认框架.
- 考虑了测量和结构部件的因子相关性.
主要成果:
- 在变量选择中,VBEM算法展示了灵活性和可靠性.
- 拟议的方法在模拟和真实数据集上都被证明是有效的.
- 部分确认框架允许有效规范化和纳入实质知识.
结论:
- 在SEM中,VBEM方法为变量选择提供了一个计算效率高的替代方案.
- 扩展的MIMIC模型为复杂的数据结构提供了灵活的框架.
- 这种方法提高了社会和心理学研究中的节性和预测准确性.
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