Center-adjusted inference for a nonparametric Bayesian random effect distribution
نویسندگان
چکیده
منابع مشابه
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 that are paired with the random effects. Similarly, the interpretation of, and inference for, the ...
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ژورنال
عنوان ژورنال: Statistica Sinica
سال: 2011
ISSN: 1017-0405
DOI: 10.5705/ss.2009.180