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Bayesian Semiparametric Structural Equation Models with Latent Variables

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  • Mingan Yang
  • David Dunson

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  • Mingan Yang & David Dunson, 2010. "Bayesian Semiparametric Structural Equation Models with Latent Variables," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 675-693, December.
    • Handle: RePEc:spr:psycho:v:75:y:2010:i:4:p:675-693
    DOI: 10.1007/s11336-010-9174-4
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    References listed on IDEAS

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    1. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
    2. 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.
    3. 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.
    4. Deborah Burr & Hani Doss, 2005. "A Bayesian Semiparametric Model for Random-Effects Meta-Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 242-251, March.
    5. Ishwaran H. & Takahara G., 2002. "Independent and Identically Distributed Monte Carlo Algorithms for Semiparametric Linear Mixed Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1154-1166, December.
    6. McLachlan, G.J. & Bean, R.W. & Ben-Tovim Jones, L., 2007. "Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5327-5338, July.
    7. Ludwig Fahrmeir & Alexander Raach, 2007. "A Bayesian Semiparametric Latent Variable Model for Mixed Responses," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 327-346, September.
    8. Hong-Tu Zhu & Sik-Yum Lee, 2001. "A Bayesian analysis of finite mixtures in the LISREL model," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 133-152, March.
    9. Asim Ansari & Raghuram Iyengar, 2006. "Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 631-657, December.
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    Cited by:

    1. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.
    2. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.
    3. Sally Paganin & Christopher J. Paciorek & Claudia Wehrhahn & Abel Rodríguez & Sophia Rabe-Hesketh & Perry de Valpine, 2023. "Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models," Journal of Educational and Behavioral Statistics, , vol. 48(2), pages 147-188, April.
    4. Bao, Junshu & Hanson, Timothy E., 2016. "A mean-constrained finite mixture of normals model," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 93-99.
    5. Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    6. Yang Li & Asim Ansari, 2014. "A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models," Management Science, INFORMS, vol. 60(5), pages 1161-1179, May.
    7. Ernesto San Martín & Alejandro Jara & Jean-Marie Rolin & Michel Mouchart, 2011. "On the Bayesian Nonparametric Generalization of IRT-Type Models," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 385-409, July.
    8. Xin-Yuan Song & Zhao-Hua Lu & Jing-Heng Cai & Edward Ip, 2013. "A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 624-647, October.
    9. Kees Montfort & Johan Oud, 2015. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 257-258, March.
    10. Kim, Gwangsu & Choi, Taeryon, 2019. "Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 68-82.
    11. Mingan Yang & Min Wang & Guanghui Dong, 2020. "Bayesian variable selection for mixed effects model with shrinkage prior," Computational Statistics, Springer, vol. 35(1), pages 227-243, March.
    12. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    13. Sylvie Tchumtchoua & Dipak Dey, 2012. "Modeling Associations Among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 670-692, October.
    14. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    15. Mohsen Maleki & Darren Wraith, 2019. "Mixtures of multivariate restricted skew-normal factor analyzer models in a Bayesian framework," Computational Statistics, Springer, vol. 34(3), pages 1039-1053, September.
    16. Yang, Mingan, 2012. "Bayesian variable selection for logistic mixed model with nonparametric random effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2663-2674.
    17. Sun Jiehuan & Warren Joshua L. & Zhao Hongyu, 2017. "A Bayesian semiparametric factor analysis model for subtype identification," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 145-158, April.

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