Kleinman K P, Ibrahim J G
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Biometrics. 1998 Sep;54(3):921-38.
In longitudinal random effects models, the random effects are typically assumed to have a normal distribution in both Bayesian and classical models. We provide a Bayesian model that allows the random effects to have a nonparametric prior distribution. We propose a Dirichlet process prior for the distribution of the random effects; computation is made possible by the Gibbs sampler. An example using marker data from an AIDS study is given to illustrate the methodology.
在纵向随机效应模型中，无论是贝叶斯模型还是经典模型，通常都假定随机效应服从正态分布。我们提供了一个贝叶斯模型，该模型允许随机效应具有非参数先验分布。我们为随机效应的分布提出了一个狄利克雷过程先验；吉布斯采样器使得计算成为可能。给出了一个使用艾滋病研究中的标记数据的例子来说明该方法。
