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Latent factor regression models for grouped outcomes

Author

Listed:
  • D. B. Woodard
  • T. M. T. Love
  • S. W. Thurston
  • D. Ruppert
  • S. Sathyanarayana
  • S. H. Swan

Abstract

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Suggested Citation

  • D. B. Woodard & T. M. T. Love & S. W. Thurston & D. Ruppert & S. Sathyanarayana & S. H. Swan, 2013. "Latent factor regression models for grouped outcomes," Biometrics, The International Biometric Society, vol. 69(3), pages 785-794, September.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:3:p:785-794
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    File URL: http://hdl.handle.net/10.1111/biom.12037
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    References listed on IDEAS

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    1. Sally W. Thurston & David Ruppert & Philip W. Davidson, 2009. "Bayesian Models for Multiple Outcomes Nested in Domains," Biometrics, The International Biometric Society, vol. 65(4), pages 1078-1086, December.
    2. Sanchez, Brisa N. & Budtz-Jorgensen, Esben & Ryan, Louise M. & Hu, Howard, 2005. "Structural Equation Models: A Review With Applications to Environmental Epidemiology," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1443-1455, December.
    3. Michael W. Browne & Robert Cudeck, 1992. "Alternative Ways of Assessing Model Fit," Sociological Methods & Research, , vol. 21(2), pages 230-258, November.
    4. Xihong Lin & Louise Ryan & Mary Sammel & Daowen Zhang & Chantana Padungtod & Xiping Xu, 2000. "A Scaled Linear Mixed Model for Multiple Outcomes," Biometrics, The International Biometric Society, vol. 56(2), pages 593-601, June.
    5. 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. Amy LaLonde & Tanzy Love & Sally W. Thurston & Philip W. Davidson, 2020. "Discovering structure in multiple outcomes models for tests of childhood neurodevelopment," Biometrics, The International Biometric Society, vol. 76(3), pages 874-885, September.
    2. Francis K. C. Hui & Samuel Müller & Alan H. Welsh, 2021. "Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models," International Statistical Review, International Statistical Institute, vol. 89(1), pages 186-206, April.

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