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Bayesian inference for generalized linear mixed models
Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability to directly acknowledge multiple levels of dependency and model different data types. For small sample sizes especially, likelihood-based inference can be unreliable with variance components being particularl...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Oxford University Press
2010
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2883299/ https://ncbi.nlm.nih.gov/pubmed/19966070 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/biostatistics/kxp053 |
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