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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Vydáno v:J Multivar Anal
Hlavní autoři: Pan, Lanfeng, Li, Yehua, He, Kevin, Li, Yanming, Li, Yi
Médium: Artigo
Jazyk:Inglês
Vydáno: 2019
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Article
On-line přístup: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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