Model Selection with Lasso in Multi-group Structural Equation Models. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Lindstrøm, Jonas Christoffer; Dahl, Fredrik A.
• Is Part Of: Structural equation modeling. Volume 27:Issue 1(2020)
• Page Start: 33
• Page End: 42
• Publication Date: 2020-01-02
• Subjects: Lasso -- structural equations modelling -- variable selection -- regularization -- multiple group analysis; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1638262
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : A Structural Equations Modeling analysis of multiple groups often involves specification of cross-group parameter equality constraints. In this paper, we present a technique for estimating the differences and equalities in parameters between groups using L1-penalized estimation (also known as the Lasso). We present the general model formulation and provide an algorithm for estimating the parameters across a range of penalization levels and a procedure for determining the amount of penalization. We also provide two case studies, one with a model including only observed variables, and one with a model with latent variables. Further, we conduct a simulation study to investigate some properties of the method.