A Unified Maximum Likelihood Approach for Analyzing Structural Equation Models With Missing Nonstandard Data
Bibliographic Data
| ID | 2330510 |
|---|---|
| Authors | Sik-Yum Lee (The Chinese University of Hong Kong, China), Sik‐yum Lee (Chinese University of Hong Kong), Xin-Yuan Song (The Chinese University of Hong Kong, China) |
| Year | 2007 |
| Volume | 35 |
| Issue | 3 |
| Pages | 352-381 |
| Publication date | 2007-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methods & Research (JOURNAL) |
| Journal identifiers | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0049124106292357 |
| OpenAlex | W2007861476 |
| Language | EN |
| Citations received | 5 |
| References cited | 43 |
In this article, the authors present a unified approach for maximum likelihood analysis of structural equation models that involve subtle model formulations and nonstandard data structures. Based on the idea of data augmentation, they describe a generic Monte Carlo expectation-maximization algorithm for estimation. They propose path sampling for computing the observed data likelihood functions that usually involve complicated integrals and show how to apply this method for computing the Bayesian information criterion for model comparison. An application of the proposed unified approach to a two-level nonlinear structural equation model with missing continuous and ordered categorical data is presented. An illustrative example with a real data set is given
Algorithm · Bayesian information criterion · Bayesian probability · Categorical variable · Data mining · Expectation–maximization algorithm · Machine learning · Maximum likelihood · Missing data · Path (computing) · Statistics · Structural equation modeling · Advanced Statistical Methods and Models · Applied Mathematics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Statistical Methods and Bayesian Inference
A Marginal Maximum Likelihood Approach for Extended Quadratic Structural Equation Modeling with Ordinal Data
Discriminant Analysis Using Mixed Continuous, Dichotomous, and Ordered Categorical Variables
Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables
BIC Extensions for Order-constrained Model Selection
ML Versus MI for Missing Data With Violation of Distribution Conditions
Statistical analysis with missing data
Advanced Structural Equation Modeling
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Monte Carlo sampling methods using Markov chains and their applications
Bayesian Analysis of Binary and Polychotomous Response Data
Equation of State Calculations by Fast Computing Machines
Bayes Factors
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Structural Equations with Latent Variables
Maximum Likelihood Analysis of Nonlinear Structural Equation Models With Dichotomous Variables
Estimating item parameters and latent ability when responses are scored in two or more nominal categories
| Citations per year | 0.26 |
|---|---|
| Citation span | 2007 - 2022 (16) |
| Highly cited | No |