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Extended Generalized Linear Latent and Mixed Model

Author

Listed:
  • Eisuke Segawa
  • Sherry Emery
  • Susan J. Curry

Abstract

The generalized linear latent and mixed modeling (GLLAMM framework) includes many models such as hierarchical and structural equation models. However, GLLAMM cannot currently accommodate some models because it does not allow some parameters to be random. GLLAMM is extended to overcome the limitation by adding a submodel that specifies a distribution of the additional random effects (Extended-GLLAMM). The extension is extremely simple to implement through the Bayesian framework with the WinBUGS software. Our approach is illustrated through the analysis of data from a youth tobacco cessation study.

Suggested Citation

  • Eisuke Segawa & Sherry Emery & Susan J. Curry, 2008. "Extended Generalized Linear Latent and Mixed Model," Journal of Educational and Behavioral Statistics, , vol. 33(4), pages 464-484, December.
    • Handle: RePEc:sae:jedbes:v:33:y:2008:i:4:p:464-484
    DOI: 10.3102/1076998607307359
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