Extended Generalized Linear Latent and Mixed Model  

Oleh:

Segawa, Eisuke ; Emery, Sherry ; Curry, Susan J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Journal Of Educational And Behavioral Statistics vol. 33 no. 4 (Dec. 2008), page 464-484.
Topik: multilevel models; GLLAMM; latent variables; Bayesian statistics; MCMC
Fulltext: 464.pdf (425.42KB)

Isi artikel

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.

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