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Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables

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  • Lee, Sik-Yum
  • Song, Xin-Yuan

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  • Lee, Sik-Yum & Song, Xin-Yuan, 2003. "Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 125-142, October.
    • Handle: RePEc:eee:csdana:v:44:y:2003:i:1-2:p:125-142
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    References listed on IDEAS

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    1. Beth Reboussin & Kung-Yee Liang, 1998. "An estimating equations approach for the LISCOMP model," Psychometrika, Springer;The Psychometric Society, vol. 63(2), pages 165-182, June.
    2. Sik-Yum Lee & Hong-Tu Zhu, 2002. "Maximum likelihood estimation of nonlinear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 189-210, June.
    3. Ulf Olsson, 1979. "Maximum likelihood estimation of the polychoric correlation coefficient," Psychometrika, Springer;The Psychometric Society, vol. 44(4), pages 443-460, December.
    4. Gerhard Arminger & Bengt Muthén, 1998. "A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm," Psychometrika, Springer;The Psychometric Society, vol. 63(3), pages 271-300, September.
    5. J.‐Q. Shi & S.‐Y. Lee, 2000. "Latent variable models with mixed continuous and polytomous data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 77-87.
    6. Jamshid Etezadi-Amoli & Roderick McDonald, 1983. "A second generation nonlinear factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 48(3), pages 315-342, September.
    7. Sik-Yum Lee & Wai-Yin Poon & P. Bentler, 1992. "Structural equation models with continuous and polytomous variables," Psychometrika, Springer;The Psychometric Society, vol. 57(1), pages 89-105, March.
    8. Roderick McDonald, 1962. "A general approach to nonlinear factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 27(4), pages 397-415, December.
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    Cited by:

    1. Zhang, Yan-Qing & Tian, Guo-Liang & Tang, Nian-Sheng, 2016. "Latent variable selection in structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 190-205.
    2. Vinícius Diniz Mayrink & Renato Valladares Panaro & Marcelo Azevedo Costa, 2021. "Structural equation modeling with time dependence: an application comparing Brazilian energy distributors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 353-383, June.
    3. Lee, Sik-Yum & Song, Xin-Yuan, 2008. "On Bayesian estimation and model comparison of an integrated structural equation model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4814-4827, June.
    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. Lee, Sik-Yum & Lu, Bin & Song, Xin-Yuan, 2006. "Assessing local influence for nonlinear structural equation models with ignorable missing data," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1356-1377, March.

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