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CENTER-ADJUSTED INFERENCE FOR A NONPARAMETRIC BAYESIAN RANDOM EFFECT DISTRIBUTION
Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an identifiability problem that complicates the interpretation of, and inference for, the fixed effects t...
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| Asıl Yazarlar: | , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
2011
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| Konular: | |
| Online Erişim: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3870168/ https://ncbi.nlm.nih.gov/pubmed/24368876 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.5705/ss.2009.180 |
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