Regularized 2SLS Estimation of Structural Equation Model Parameters
Bibliographic Data
| ID | 21641702 |
|---|---|
| Authors | Steffen Nestler (0000-0001-9724-2441, University of Münster, corresponding author) |
| Year | 2022 |
| Volume | 29 |
| Issue | 6 |
| Pages | 920-932 |
| Publication date | 2022-11-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2022.2069114 |
| OpenAlex | W4283024421 |
| Language | EN |
| References cited | 49 |
There is a growing interest among methodological and applied researchers in extending the use of regularization techniques (e.g., lasso regression, the elastic net) to structural equation models (SEMs). To date, most of the extensions have been based on combining the respective penalty function with the standard Maximum Likelihood fit function for SEMs. In the present article, we describe two ways in which the Two-Stage Least Squares (2SLS) estimator, an equation-by-equation estimator of SEMs, can be combined with these regularization techniques. Both approaches can be used to regularize the parameters in single equations (“local” regularization), and for both approaches, the parameters can be determined very quickly and efficiently using standard software. We evaluated the two methods in two simulation studies. We were able to show that both approaches provide suitable parameter estimates and can be used to select factor models and path coefficients even when the model is incorrectly specified
Algorithm · Elastic net regularization · Estimator · Least-squares function approximation · Mathematical optimization · Regression · Statistics · Structural equation modeling · Computer Science · Mathematics · Mental Health Research Topics · Multi-Criteria Decision Making · Psychometric Methodologies and Testing · Applied Mathematics · Artificial Intelligence
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An Alternative Two Stage Least Squares (2SLS) Estimator for Latent Variable Equations
Latent Variable Modeling in Heterogeneous Populations
Regularization Paths for Generalized Linear Models via Coordinate Descent
Structural Equations with Latent Variables
Can Ridge and Elastic Net Structural Equation Modeling be Used to Stabilize Parameter Estimates when Latent Factors are Correlated
A Comparison of Regularized Maximum-Likelihood, Regularized 2-Stage Least Squares, and Maximum-Likelihood Estimation with Misspecified Models, Small Samples, and Weak Factor Structure
Model Implied Instrumental Variables (MIIVs)
A Limited Information Estimator for Dynamic Factor Models
Presidential Address
Latent Variable Models Under Misspecification
Automating the Selection of Model-Implied Instrumental Variables
Assessing the Effect of Model Misspecifications on Parameter Estimates in Structural Equation Models
Principles and Practice of Structural Equation Modeling
| Highly cited | No |
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