Maximum likelihood estimation of nonlinear structural equation models

By Sik-Yum Lee, Hong-Tu Zhu. 2002

108 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/15d55f2a2e61ccaa93515f1f133e31219be89471

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In Artificial Intelligence

Citations in the corpus

Latent Variable Modelling: A Survey * (2007, background): “Arminger & Muthén (1998) and Lee & Zhu (2002), among others, discuss nonlinear versions of the LISREL model for continuous responses (see section 2.2).”
On Insensitivity of the Chi-Square Model Test to Nonlinear Misspecification in Structural Equation Models (2008, background): “There are several ways to tackle this problem, see Klein and Moosbrugger (2000), Lee and Zhu (2002), and Klein (2007), to mention just a few publications.”
Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data (2006, methodology): “Statistical analysis of this NSEM has been studied by Lee and Zhu (2000, 2002) , on the basis of fully observed data.”
A Semiparametric Approach to Modeling Nonlinear Relations Among Latent Variables (2005, methodology): “Third, it would be useful to systematically compare this approach for semiparametrically modeling nonlinear latent variable relations to the flurry of new parametric approaches recently presented in the structural equation modeling literature (e.g., Arminger & Muthen, 1998; Klein & Moosbrugger, 2000”
An Alternative Approach for Nonlinear Latent Variable Models (2010, unknown): “There are several ways to tackle this problem (see Cudeck, Harring, & du Toit, 2009 ; Klein, 2007 ; Klein & Moosbrugger, 2000 ; Lee & Zhu, 2002 ; as well as quasi-likelihood methods making fewer assumptions, Klein & Muthen, 2007 ).”
Model comparison of nonlinear structural equation models with fixed covariates (2003, methodology): “also been developed for various nonlinear SEMs, see Arminger and Muthdn (1998), Zhu and Lee (1999), and Lee and Zhu (2002) for analysis of models with continuous variables, and Lee and Zhu (2000) for analysis of models with mixed continuous and polytomous variables.”
Structural equation modeling: strengths, limitations, and misconceptions. (2005, background): “On the positive side, we should add that latent variable interaction modeling is a very active area of research that has yielded several promising new developments in recent years (e.g., Jöreskog 2000; Klein & Moosbrugger 2000; Lee & Zhu 2002; Lee et al. 2004; Wall & Amemiya 2000, 2001).”