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Latent Transition Regression for Mixed Outcomes

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  • Diana L. Miglioretti

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  • Diana L. Miglioretti, 2003. "Latent Transition Regression for Mixed Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 710-720, September.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:3:p:710-720
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    File URL: http://hdl.handle.net/10.1111/1541-0420.00082
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    References listed on IDEAS

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    1. Beth A. Reboussin & Kung-Yee Liang & David M. Reboussin, 1999. "Estimating Equations for a Latent Transit ion Model with Multiple Discrete Indicators," Biometrics, The International Biometric Society, vol. 55(3), pages 839-845, September.
    2. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
    3. Jason Roy & Xihong Lin, 2000. "Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes," Biometrics, The International Biometric Society, vol. 56(4), pages 1047-1054, December.
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    Cited by:

    1. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
    2. Silvia Cagnone & Cinzia Viroli, 2018. "Multivariate latent variable transition models of longitudinal mixed data: an analysis on alcohol use disorder," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1399-1418, November.
    3. Dine, Abdessamad & Larocque, Denis & Bellavance, François, 2009. "Multivariate trees for mixed outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3795-3804, September.
    4. Chih-chiang Yang, 2007. "Confirmatory and Structural Categorical Latent Variables Models," Quality & Quantity: International Journal of Methodology, Springer, vol. 41(6), pages 831-849, December.

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