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The Prowess and Pitfalls of Bayesian Structural Equation Modeling
Journal of Management, 2014Muthén and Asparouhov introduced an approach for conducting Bayesian inference in the context of structural equation models that they termed Bayesian structural equation modeling (BSEM). In this article, we provide an overview of the BSEM technique, illustrate how this technique relates to confirmatory and exploratory factor analysis, and highlight ...
William R. Stromeyer +3 more
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A tutorial on Bayesian structural equation modelling: Principles and applications
International Journal of PsychologyThis paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on ...
Qijin Chen +5 more
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Parameter estimation in Bayesian Structural Equation Modeling
2013In this study Bayesian Estimation Method, which is one of the most common methods of structural equation models in recent years for using parameter estimation, were discussed. In compliance with the flexibility of Baesian approaches, the usage of Markov Chain Monte Carlo methods were considered for structural equation models and hidden variables. Gibbs
Şehribanoğlu, Sanem, Okut, Hayrettin
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Bayesian analysis of stochastic constraints in structural equation models
British Journal of Mathematical and Statistical Psychology, 1992Structural equation models are analysed in the presence of stochastic constraints. Based on a Bayesian perspective, a prior distribution on nuisance parameters in the unknown covariance matrix of error measurements with stochastic constraints is considered.
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A Robust Bayesian Approach for Structural Equation Models with Missing Data
Psychometrika, 2008In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data.
Lee, Sik-Yum, Xia, Ye-Mao
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A Bayesian structural equation model in general pedigree data analysis
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2019AbstractStructural equation modeling (SEM) is a powerful, comprehensive, and flexible multivariate statistical method for modeling relationships between observed and latent variables. However, in genetic association analysis, frequentist approaches to fitting SEMs do not always lead to convergence and admissible solutions for complex models ...
Mahdi Akbarzadeh +5 more
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Bayesian empirical likelihood estimation of quantile structural equation models
Journal of Systems Science and Complexity, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yanqing Zhang 0004, Nian-Sheng Tang
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Bayesian model selection for structural equation models for myopia data
Communications in Statistics - Simulation and Computation, 2022Tsai-Hung Fan, Yi-Fu Wang
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On Bayesian structural inference in a simultaneous equation model [PDF]
, 2002 Econometric issues that are considered fundamental in the development of Bayesian structural inference within a Simultaneous Equation Model are surveyed. The difficulty of specifying prior information which is of interest to economists and which yields tractable posterior and predictive distributions has started this line of research.
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