Regularized Structural Equation Models

Regularized estimation methods have become increasingly popular with the advent of 'Big Data' and machine learning methods. Regularized estimation purposefully introduces a small bias in order to stabilize parameter estimates. This is especially desirable when complex models are estimated in relatively small samples. In recent years, structural equation models were combined with regularization and in our research we mainly examine the statistical properties of such combinations and also evaluate their usefulness relative to other analysis method.

Representative publications:

Nestler, S. (in press). Regularized 2SLS estimation of structural equation model parameters. Structural Equation Modeling: A Multidisciplinary Journal.

Scharf, F., & Nestler, S. (2019c). Should regularization replace simple structure rotation in Exploratory Factor Analysis? Structural Equation Modeling: A Multidisciplinary Journal, 26, 576-590.

Scharf, F., Pförtner, J., & Nestler, S. (2021). Can ridge and elastic net structural equation modeling be used to stabilize parameter estimates when latent factors are correlated? Structural Equation Modeling: A Multidisciplinary Journal, 28, 928-940