Bayesian estimation and testing of structural equation models

DOI10.1007/BF02294318zbMath1291.62244MaRDI QIDQ2250658

Herbert Hoijtink, Anne Boomsma, Richard Scheines

Publication date: 18 July 2014

Published in: Psychometrika (Search for Journal in Brave)

zbMATH Keywords

Gibbs samplerBayesian inferencestructural equation modelsposterior predictive \(p\)-values

Mathematics Subject Classification ID

Bayesian inference (62F15) Applications of statistics to psychology (62P15)

Related Items (15)

Bayesian analysis of the functional-coefficient autoregressive heteroscedastic model ⋮ Assessment of generalised Bayesian structural equation models for continuous and binary data ⋮ Dealing with reflection invariance in Bayesian factor analysis ⋮ A criterion-based model comparison statistic for structural equation models with heterogeneous data ⋮ Bayesian structural equation modeling for the health index ⋮ Heterogeneous factor analysis models: a Bayesian approach ⋮ A robust Bayesian approach for structural equation models with missing data ⋮ Bayesian analysis of nonlinear structural equation models with nonignorable missing data ⋮ Finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research ⋮ A Bayesian analysis of finite mixtures in the LISREL model ⋮ On Bayesian estimation and model comparison of an integrated structural equation model ⋮ Book review of: S.-Y. Lee and X.-Y. Song, Basic and advanced Bayesian structural equation modeling. With applications in the medical and behavioral sciences ⋮ Bayesian analysis of multi-group nonlinear structural equation models with application to behavioral finance ⋮ Expert statistical testimony and epidemiological evidence: The toxic effects of lead exposure on children ⋮ Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity

Uses Software

• LISREL
• EQS
• BayesDA
• TETRAD

Cites Work

• Regressor diagnostics for the classical errors-in-variables model
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• Causation, prediction, and search
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• Posterior predictive \(p\)-values
• Markov chains for exploring posterior distributions. (With discussion)
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• Consistent Sets of Estimates for Regressions with Errors in All Variables
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Bootstrap‐corrected ADF test statistics in covariance structure analysis
• Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions.
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