Jacobucci Ross, Grimm Kevin J, McArdle John J
University of Southern California.
Arizona State University.
Struct Equ Modeling. 2016;23(4):555-566. doi: 10.1080/10705511.2016.1154793. Epub 2016 Apr 12.
A new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regularization has gained wide adoption in regression, very little has transferred to models with latent variables. By adding penalties to specific parameters in a structural equation model, researchers have a high level of flexibility in reducing model complexity, overcoming poor fitting models, and the creation of models that are more likely to generalize to new samples. The proposed method was evaluated through a simulation study, two illustrative examples involving a measurement model, and one empirical example involving the structural part of the model to demonstrate RegSEM's utility.
提出了一种新方法，该方法将套索回归和岭回归中的正则化应用扩展到结构方程模型。该方法被称为正则化结构方程建模（RegSEM）。RegSEM对结构方程模型中的特定参数进行惩罚，目的是创建更易于理解和更简单的模型。尽管正则化在回归中已被广泛采用，但很少应用于具有潜在变量的模型。通过在结构方程模型中对特定参数添加惩罚，研究人员在降低模型复杂性、克服拟合不佳的模型以及创建更有可能推广到新样本的模型方面具有高度的灵活性。通过模拟研究、两个涉及测量模型的示例以及一个涉及模型结构部分的实证示例对所提出的方法进行了评估，以证明RegSEM的效用。
