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Detecting Prior-Data Disagreement in Bayesian Structural Equation Modeling
Structural Equation Modeling, 2022 Supplemental Materials for a Manuscript Titled: "Detecting Prior-Data Disagreement in Bayesian Structural Equation ...
Sarah Depaoli, Sonja D Winter
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Comparison of Frequentist and Bayesian Regularization in Structural Equation Modeling [PDF]
Structural Equation Modeling, 2018 Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while
Ross Jacobucci, Kevin J. Grimm
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Bayesian structural equation model
WIREs Computational Statistics, 2014Latent variables that should be measured by multiple observed variable are common in substantive research. Structural equation models (SEMs), which can be regarded as regression models with observed and latent variables, are useful models to assess interrelationships among these variables and have been widely applied to many fields.
Lee, Sik-Yum, Song, Xin-Yuan
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Bayesian Estimation and Testing of Structural Equation Models
Psychometrika, 1999The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples.
Scheines, R. +2 more
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Modeling Misspecification as a Parameter in Bayesian Structural Equation Models
Educational and Psychological Measurement, 2023Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter—a parameter akin to the correlation root mean squared residual.
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A semiparametric Bayesian approach for structural equation models
Biometrical Journal, 2010AbstractIn the development of structural equation models (SEMs), observed variables are usually assumed to be normally distributed. However, this assumption is likely to be violated in many practical researches. As the non‐normality of observed variables in an SEM can be obtained from either non‐normal latent variables or non‐normal residuals or both ...
Song, Xin-Yuan +5 more
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Bayesian Structural Equation Modeling Method for Hierarchical Model Validation
Reliability Engineering and System Safety, 2009A building block approach to model validation may proceed through various levels, such as material to component to subsystem to system, comparing model predictions with experimental observations at each level. Usually, experimental data becomes scarce as one proceeds from lower to higher levels.
Sankaran Mahadevan
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