Department of Psychology, University of Notre Dame.
Psychol Methods. 2022 Aug;27(4):497-518. doi: 10.1037/met0000389. Epub 2021 Jan 28.
Regularization methods such as the least absolute shrinkage and selection operator (LASSO) are commonly used in high dimensional data to achieve sparser solutions. Recently, methods such as regularized structural equation modeling (SEM) and penalized likelihood SEM have been proposed, trying to transfer the benefits of regularization to models commonly used in social and behavioral research. These methods allow researchers to estimate large models even in the presence of small sample sizes. However, some drawbacks of the LASSO, such as high false positive rates (FPRs) and inconsistency in selection results, persist at the same time. We propose the application of stability selection, a method based on repeated resampling of the data to select stable coefficients, to regularized SEM as a mechanism to overcome these limitations. Across 2 simulation studies, we find that stability selection greatly improves upon the LASSO in selecting the correct paths, specifically through reducing the number of false positives. We close the article by demonstrating the application of stability selection in 2 empirical examples and presenting several future research directions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
正则化方法，如最小绝对收缩和选择算子（LASSO），常用于高维数据中以实现更稀疏的解。最近，提出了正则化结构方程模型（SEM）和惩罚似然 SEM 等方法，试图将正则化的好处转移到社会和行为研究中常用的模型上。这些方法允许研究人员在小样本量的情况下估计大型模型。然而，LASSO 的一些缺点，如高假阳性率（FPR）和选择结果不一致，仍然存在。我们提出将稳定性选择应用于正则化 SEM，这是一种基于数据重复重采样以选择稳定系数的方法，作为克服这些限制的机制。通过两项模拟研究，我们发现稳定性选择在选择正确路径方面大大优于 LASSO，特别是通过减少假阳性的数量。文章最后通过展示稳定性选择在两个实证示例中的应用，并提出了几个未来的研究方向，结束了这篇文章。（PsycInfo 数据库记录（c）2022 APA，保留所有权利）。
