A semiparametric Bayesian approach to the random effects model
A semiparametric Bayesian approach to the random effects model
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DOI：
10.2307/2533846
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发表时间：
1998-09-01
期刊：
影响因子：
1.9
通讯作者：
Ibrahim, JG
中科院分区：
文献类型：
--
作者：
Kleinman, KP;Ibrahim, JG
在纵向随机效应模型中，随机效应通常被假设为在贝叶斯模型和经典模型中具有正态分布。我们提供了一个贝叶斯模型，允许随机效应具有非参数先验分布。我们提出了一个Dirichlet过程之前的随机效应的分布，计算是可能的吉布斯采样器。一个例子使用标记数据从艾滋病的研究来说明的方法。
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.