Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables

DOI10.1016/S0167-9473(02)00305-5zbMath1429.62647OpenAlexW2003058911MaRDI QIDQ956739

Xin-Yuan Song, Sik-Yum Lee

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00305-5

zbMATH Keywords

importance samplingBayesian information criterionGibbs samplerlatent variablesMetropolis-Hastings algorithmMCEM algorithm

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Point estimation (62F10) Bayesian inference (62F15) Applications of statistics to psychology (62P15)

Related Items (1)

Assessing local influence for nonlinear structural equation models with ignorable missing data

Uses Software

• LISREL
• EQS
• tsbridge

Cites Work

• Maximum likelihood estimation of nonlinear structural equation models
• A second generation nonlinear factor analysis
• Maximum likelihood estimation of the polychoric correlation coefficient
• Structural equation models with continuous and polytomous variables
• An estimating equations approach for the LISCOMP model
• STEMM: A general finite mixture structural equation model
• A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hastings algorithm
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The simultaneous estimation of factor loadings and scores
• Analysis of multivariate probit models
• Fitting Full-Information Item Factor Models and an Empirical Investigation of Bridge Sampling
• A two‐stage estimation of structural equation models with continuous and polytomous variables
• Bayes Factors
• Latent Variable Models with Mixed Continuous and Polytomous Data
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
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