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Generalized Linear Mixed Models with Gaussian Mixture Random Effects: Inference and Application
We propose a new class of generalized linear mixed models with Gaussian mixture random effects for clustered data. To overcome the weak identifiability issues, we fit the model using a penalized Expectation Maximization (EM) algorithm, and develop sequential locally restricted likelihood ratio tests...
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| Pubblicato in: | J Multivar Anal |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2019
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7021245/ https://ncbi.nlm.nih.gov/pubmed/32063658 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jmva.2019.104555 |
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