Fitting multivariage normal finite mixtures subject to structural equation modeling

DOI10.1007/BF02294853zbMath1291.62201OpenAlexW2171460817MaRDI QIDQ2248658

Conor V. Dolan, Han L. J. van der Maas

Publication date: 27 June 2014

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02294853

zbMATH Keywords

quasi-Newtonstructural equation modelingLISRELmultivariate normal mixtures

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (16)

Bayesian diagnostics of transformation structural equation models ⋮ Latent profile analysis with nonnormal mixtures: a Monte Carlo examination of model selection using fit indices ⋮ Heteroscedastic latent trait models for dichotomous data ⋮ Robust fitting of mixtures of factor analyzers using the trimmed likelihood estimator ⋮ A criterion-based model comparison statistic for structural equation models with heterogeneous data ⋮ Nonlinear regime-switching state-space (RSSS) models ⋮ Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data ⋮ Structural Equation Models for Finite Mixtures — Simulation Results and Empirical Applications ⋮ Testing for measurement invariance with respect to an ordinal variable ⋮ A mixture of generalized latent variable models for mixed mode and heterogeneous data ⋮ Response mixture modeling: accounting for heterogeneity in item characteristics across response times ⋮ Latent Variable Modelling: A Survey* ⋮ A Bayesian analysis of finite mixtures in the LISREL model ⋮ Bayesian analysis for confirmatory factor model with finite-dimensional Dirichlet prior mixing ⋮ Mixtures of factor analyzers: an extension with covariates ⋮ Tests for statistical significance of a treatment effect in the presence of hidden sub-populations

Uses Software

• LISREL

Cites Work

• Analysis of time series subject to changes in regime
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• STEMM: A general finite mixture structural equation model
• Finite mixtures in confirmatory factor-analysis models
• A mixture likelihood approach for generalized linear models
• Simultaneous factor analysis in several populations
• Maximum-Likelihood Estimation of Parameters Subject to Restraints
• Maximum likelihood estimates of the parameters of a mixture of two regression lines
• Maximum Likelihood Estimation and Goodness-of-fit Tests for Mixtures of Distributions
• A GENERAL METHOD FOR STUDYING DIFFERENCES IN FACTOR MEANS AND FACTOR STRUCTURE BETWEEN GROUPS
• ESTIMATION AND TESTING OF SIMPLEX MODELS
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