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Hidden Markov latent variable models with multivariate longitudinal data

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  • Xinyuan Song
  • Yemao Xia
  • Hongtu Zhu

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  • 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.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:313-323
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    File URL: http://hdl.handle.net/10.1111/biom.12536
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    References listed on IDEAS

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    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. Antonello Maruotti, 2011. "Mixed Hidden Markov Models for Longitudinal Data: An Overview," International Statistical Review, International Statistical Institute, vol. 79(3), pages 427-454, December.
    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. Edward Ip & Qiang Zhang & Jack Rejeski & Tammy Harris & Stephen Kritchevsky, 2013. "Partially Ordered Mixed Hidden Markov Model for the Disablement Process of Older Adults," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 370-384, June.
    5. Scott, Steven L. & James, Gareth M. & Sugar, Catherine A., 2005. "Hidden Markov Models for Longitudinal Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 359-369, June.
    6. Jeroen K. Vermunt & Rolf Langeheine & Ulf Bockenholt, 1999. "Discrete-Time Discrete-State Latent Markov Models with Time-Constant and Time-Varying Covariates," Journal of Educational and Behavioral Statistics, , vol. 24(2), pages 179-207, June.
    7. 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.
    8. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
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    Citations

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    Cited by:

    1. Qi Zhang & Yihui Zhang & Yemao Xia, 2024. "Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations," Mathematics, MDPI, vol. 12(5), pages 1-23, March.
    2. Lin, Yiqi & Song, Xinyuan, 2022. "Order selection for regression-based hidden Markov model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    3. Liu, Hefei & Song, Xinyuan, 2021. "Bayesian analysis of hidden Markov structural equation models with an unknown number of hidden states," Econometrics and Statistics, Elsevier, vol. 18(C), pages 29-43.
    4. Zhou, Jie & Song, Xinyuan & Sun, Liuquan, 2020. "Continuous time hidden Markov model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    5. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    6. Daniel R. Kowal & Bohan Wu, 2023. "Semiparametric count data regression for self‐reported mental health," Biometrics, The International Biometric Society, vol. 79(2), pages 1520-1533, June.
    7. Liu, Hefei & Song, Xinyuan & Zhang, Baoxue, 2022. "Varying-coefficient hidden Markov models with zero-effect regions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    8. Xia, Ye-Mao & Tang, Nian-Sheng, 2019. "Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 190-211.

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