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Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data

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  • Sik-Yum Lee
  • Xin-Yuan Song

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  • Sik-Yum Lee & Xin-Yuan Song, 2004. "Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data," Biometrics, The International Biometric Society, vol. 60(3), pages 624-636, September.
    • Handle: RePEc:bla:biomet:v:60:y:2004:i:3:p:624-636
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2004.00211.x
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    References listed on IDEAS

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    1. Jian Qing Shi & John Copas, 2002. "Publication bias and meta‐analysis for 2×2 tables: an average Markov chain Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 221-236, May.
    2. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    3. Sik-Yum Lee & Jian-Qing Shi, 2001. "Maximum Likelihood Estimation of Two-Level Latent Variable Models with Mixed Continuous and Polytomous Data," Biometrics, The International Biometric Society, vol. 57(3), pages 787-794, September.
    4. 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.
    5. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    6. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
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    Cited by:

    1. Sik-Yum Lee & Ye-Mao Xia, 2006. "Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 71(3), pages 565-585, September.
    2. Xinyuan Song & Yemao Xia & Hongtu Zhu, 2017. "Hidden Markov latent variable models with multivariate longitudinal data," Biometrics, The International Biometric Society, vol. 73(1), pages 313-323, March.
    3. Celine Marielle Laffont & Marc Vandemeulebroecke & Didier Concordet, 2014. "Multivariate Analysis of Longitudinal Ordinal Data With Mixed Effects Models, With Application to Clinical Outcomes in Osteoarthritis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 955-966, September.
    4. 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.
    5. 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.
    6. Fu, Ying-Zi & Tang, Nian-Sheng & Chen, Xing, 2009. "Local influence analysis of nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3671-3684, August.
    7. Ling Zhou & Huazhen Lin & Xinyuan Song & Yi Li, 2014. "Selection of Latent Variables for Multiple Mixed-outcome Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1064-1082, December.
    8. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.
    9. B. N. Sánchez & E. A. Houseman & L. M. Ryan, 2009. "Residual-Based Diagnostics for Structural Equation Models," Biometrics, The International Biometric Society, vol. 65(1), pages 104-115, March.
    10. Chen, Xue-Dong & Fu, Ying-Zi, 2011. "Model selection for zero-inflated regression with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 765-773, January.
    11. Chunjie Wang & Bo Zhao & Linlin Luo & Xinyuan Song, 2021. "Regression analysis of current status data with latent variables," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 413-436, July.

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