Theory and method for constrained estimation in structural equation models with incomplete data.

DOI10.1016/S0167-9473(98)00015-2zbMath1042.62528OpenAlexW2021944482MaRDI QIDQ1129098

Man-Lai Tang, Peter M. Bentler

Publication date: 13 August 1998

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

Full work available at URL: https://doi.org/10.1016/s0167-9473(98)00015-2

zbMATH Keywords

EM algorithmMissing dataStructural equation modelsConstrained maximum likelihood estimation

Mathematics Subject Classification ID

Related Items (4)

Normal distribution based pseudo ML for missing data: with applications to mean and covariance structure analysis ⋮ Tests of homogeneity of means and covariance matrices for multivariate incomplete data ⋮ Missing data mechanisms and homogeneity of means and variances-covariances ⋮ Identifying variables responsible for data not missing at random

Uses Software

• LISREL
• Amos
• EQS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimation for structural equation models with missing data
• On structural equation modeling with data that are not missing completely at random
• A study of algorithms for covariance structure analysis with specific comparisons using factor analysis
• Estimation for the multiple factor model when data are missing
• A simple Gauss-Newton procedure for covariance structure analysis with high-level computer languages
• On testing functional constraints in structural equation models
• Constrained estimation in covariance structure analysis
• The Restricted EM Algorithm for Maximum Likelihood Estimation Under Linear Restrictions on the Parameters
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• Missing Observations in Multivariate Statistics. III: Large Sample Analysis of Simple Linear Regression

This page was built for publication: Theory and method for constrained estimation in structural equation models with incomplete data.