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Maximum Likelihood Estimation and Model Comparison for Mixtures of Structural Equation Models with Ignorable Missing Data

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  • Sik-Yum Lee
  • Xin-Yuan Song

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  • Sik-Yum Lee & Xin-Yuan Song, 2003. "Maximum Likelihood Estimation and Model Comparison for Mixtures of Structural Equation Models with Ignorable Missing Data," Journal of Classification, Springer;The Classification Society, vol. 20(2), pages 221-255, September.
    • Handle: RePEc:spr:jclass:v:20:y:2003:i:2:p:221-255
    DOI: 10.1007/s00357-003-0013-5
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    Cited by:

    1. Sik-Yum Lee & Xin-Yuan Song, 2007. "A Unified Maximum Likelihood Approach for Analyzing Structural Equation Models With Missing Nonstandard Data," Sociological Methods & Research, , vol. 35(3), pages 352-381, February.
    2. Erik Meijer & Susann Rohwedder & Tom Wansbeek, 2008. "Prediction of Latent Variables in a Mixture of Structural Equation Models, with an Application to the Discrepancy Between Survey and Register Data," Working Papers WR-584, RAND Corporation.
    3. Erik Meijer & Susann Rohwedder & Tom Wansbeek, 2008. "Prediction of Latent Variables in a Mixture of Structural Equation Models, with an Application to the Discrepancy Between Survey and Register Data," Working Papers 584, RAND Corporation.
    4. Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
    5. Tang, Nian-Sheng & Chen, Xing & Fu, Ying-Zi, 2009. "Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2031-2043, October.

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