Bayesian analysis for finite mixture in non-recursive non-linear structural equation models

2010 ◽  
Vol 63 (2) ◽  
pp. 361-377 ◽  
Author(s):  
Yong Li ◽  
Hai-Zhong Wang
Keyword(s):  
Bayesian Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Finite Mixture ◽  
Non Linear

Non-linear structural equation models with correlated continuous and discrete data

2009 ◽  
Vol 62 (2) ◽  
pp. 327-347 ◽  
Author(s):  
Sik-Yum Lee ◽  
Xin-Yuan Song ◽  
Jing-Heng Cai ◽  
Wing-Yee So ◽  
Ching-Wang Ma ◽  
...  
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Discrete Data ◽  
Non Linear

scholarly journals A two-stage estimation procedure for non-linear structural equation models

Biostatistics ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 676-691
Author(s):  
Klaus Kähler Holst ◽  
Esben Budtz-Jørgensen
Keyword(s):  
Linear Model ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Outcome Variable ◽  
Two Stage ◽  
Ml Estimation ◽  
Linear Effect ◽  
Non Linear ◽  
Stage Estimation

Summary Applications of structural equation models (SEMs) are often restricted to linear associations between variables. Maximum likelihood (ML) estimation in non-linear models may be complex and require numerical integration. Furthermore, ML inference is sensitive to distributional assumptions. In this article, we introduce a simple two-stage estimation technique for estimation of non-linear associations between latent variables. Here both steps are based on fitting linear SEMs: first a linear model is fitted to data on the latent predictor and terms describing the non-linear effect are predicted by their conditional means. In the second step, the predictions are included in a linear model for the latent outcome variable. We show that this procedure is consistent and identifies its asymptotic distribution. We also illustrate how this framework easily allows the association between latent variables to be modeled using restricted cubic splines, and we develop a modified estimator which is robust to non-normality of the latent predictor. In a simulation study, we compare the proposed method to MLE and alternative two-stage estimation techniques.

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