ABSTRACT

The lasso is a commonly used regularization method that is increasing used in structural equation models (SEMs). Under the Bayesian framework, lasso is rendered more flexible and readily produces estimates of standard errors and the penalty parameter. However, in practice, it remains unclear what decision rule is appropriate for parameter identification; in other words, determining what size estimate is large enough to be included into the model. The current study compared three decision rules for parameter identification – thresholding, p-value, and credible interval in confirmatory factor analysis. Specifically, two distinct parameter spaces were studied: cross-loadings and residual correlations. Results showed that the thresholding rule performed best in balancing power and Type I error rate. Different thresholds for standardized estimates were needed for different conditions. Guidelines for parameter identification and recommended thresholding values were also provided. Results of the current study have the potential to extend to a broad range of SEMs.

摘要

套索是一种常用的正则化方法，越来越多地用于结构方程模型 (SEM)。在贝叶斯框架下，套索变得更加灵活并且容易产生标准误差和惩罚参数的估计。然而，在实践中，尚不清楚什么样的决策规则适用于参数识别；换句话说，确定什么大小估计足够大以包含在模型中。目前的研究比较了参数识别的三个决策规则——阈值，p-值，以及验证性因素分析中的可信区间。具体来说，研究了两个不同的参数空间：交叉加载和残差相关性。结果表明，阈值规则在平衡功率和 I 类错误率方面表现最好。不同条件需要不同的标准化估计阈值。还提供了参数识别指南和推荐的阈值。当前研究的结果有可能扩展到广泛的 SEM。