A Note on the Connection Between Trek Rules and Separable Nonlinear Least Squares in Linear Structural Equation Models.

ABSTRACT: We show that separable nonlinear least squares (SNLLS) estimation is applicable to all linear structural equation models (SEMs) that can be specified in RAM notation. SNLLS is an estimation technique that has successfully been applied to a wide range of models, for example neural networks and dynamic systems, often leading to improvements in convergence and computation time. It is applicable to models of a special form, where a subset of parameters enters the objective linearly. Recently, Kreiberg et al. (Struct Equ Model Multidiscip J 28(5):725-739, 2021. https://doi.org/10.1080/10705511.2020.1835484 ) have shown that this is also the case for factor analysis models. We generalize this result to all linear SEMs. To that end, we show that undirected effects (variances and covariances) and

SUBMITTER: Ernst MS 

PROVIDER: S-EPMC9977899 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature