ML Estimation of Mean and Covariance Structures with Missing Data Using Complete Data Routines
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DOI: 10.3102/10769986024001021
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Cited by:
- Sik-Yum Lee, 2006. "Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 71(3), pages 541-564, September.
- Mortaza Jamshidian & Siavash Jalal, 2010. "Tests of Homoscedasticity, Normality, and Missing Completely at Random for Incomplete Multivariate Data," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 649-674, December.
- Xin-Yuan Song & Sik-Yum Lee, 2002. "Analysis of structural equation model with ignorable missing continuous and polytomous data," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 261-288, June.
- Erik Meijer & Arie Kapteyn & Tatiana Andreyeva, 2008. "Health Indexes and Retirement Modeling in International Comparisons," Working Papers 614, RAND Corporation.
- Jamshidian, Mortaza & Jalal, Siavash & Jansen, Camden, 2014. "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i06).
- Erik Meijer & Arie Kapteyn & Tatiana Andreyeva, 2008. "Health Indexes and Retirement Modeling in International Comparisons," Working Papers WR-614, RAND Corporation.
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Keywords
factor analysis; incomplete data; listwise deletion; mean imputation; missing data mechanism; observed information; test of hypothesis;All these keywords.
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