A distribution-free method for structural equation models with incomplete data
DOI10.1080/03610928708829427zbMath0614.62077OpenAlexW1997639565MaRDI QIDQ4721427
Publication date: 1987
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610928708829427
computer programmissing observationsGauss-Newton algorithmstructural equation modelselliptical distributionconfirmatory factor analysisasymptotically distribution-freegeneralized least squares approach
Multivariate analysis (62H99) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Nonparametric estimation (62G05)
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Cites Work
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- Some contributions to efficient statistics in structural models: Specification and estimation of moment structures
- Least-squares theory based on general distributional assumptions with an application to the incomplete observations problem
- Estimation for structural equation models with missing data
- Estimation for the multiple factor model when data are missing
- Structural analysis of covariance and correlation matrices
- Linear structural equations with latent variables
- Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
- A Method of Generating Best Asymptotically Normal Estimates with Application to the Estimation of Bacterial Densities
- Alternative Approaches to Missing Values in Discriminant Analysis
- Asymptotically distribution‐free methods for the analysis of covariance structures
- Measures of multivariate skewness and kurtosis with applications
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