5. Three Likelihood-Based Methods for Mean and Covariance Structure Analysis with Nonnormal Missing Data
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- Type
- article
- Published
- 2000-08-01
- Cited by
- 1,798
- References
- 67
- OpenAlex
- https://openalex.org/W2095968500
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:123677162
Keywords
Estimator, Covariance, Statistics, Missing data, Mathematics
References
- Robust Estimation of the Mean and Covariance Matrix from Data with Missing Values
- Latent variables in linear stochastic models : refletions on "maximum likelihood" and "partial least squares" methods
- Model Conditions for Asymptotic Robustness in the Analysis of Linear Relations
- Lisrel 8: User's Reference Guide
- Multiple-Imputation Inferences with Uncongenial Sources of Input
- Principles and Practice of Structural Equation Modeling
- Mean and Covariance Structure Analysis: Theoretical and Practical Improvements
- Asymptotic comparison of missing data procedures for estimating factor loadings
- The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.
- Influence in covariance structure analysis : with an application to confirmatory factor analysis
- Asymptotic distributions in canonical correlation analysis and other multivariate procedures for nonnormal populations
- Estimation for the multiple factor model when data are missing
- Normal theory based test statistics in structural equation modelling.
- Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods
- Statistical Analysis With Missing Data
- Asymptotics of Estimating Equations under Natural Conditions
- The asymptotic normal distribution of estimators in factor analysis under general conditions
- A Course in Large Sample Theory
- Robustness of normal theory statistics in structural equation models
- ML Estimation of Mean and Covariance Structures with Missing Data Using Complete Data Routines
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