Kinney Satkartar K, Dunson David B
Institute of Statistics and Decision Sciences, Duke University, Box 90251, Durham, North Carolina 27705, USA.
Biometrics. 2007 Sep;63(3):690-8. doi: 10.1111/j.1541-0420.2007.00771.x. Epub 2007 Apr 2.
We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is implemented using a stochastic search Gibbs sampler to estimate the exact model-averaged posterior distribution. This approach automatically identifies subsets of predictors having nonzero fixed effect coefficients or nonzero random effects variance, while allowing uncertainty in the model selection process. Default priors are proposed for the variance components and an efficient parameter expansion Gibbs sampler is developed for posterior computation. The approach is illustrated using simulated data and an epidemiologic example.
我们解决了在相关数据的逻辑混合效应模型的固定和随机成分中选择应包含哪些变量的问题。使用随机搜索吉布斯采样器实现了一种完全贝叶斯变量选择，以估计精确的模型平均后验分布。这种方法自动识别具有非零固定效应系数或非零随机效应方差的预测变量子集，同时允许模型选择过程中的不确定性。针对方差成分提出了默认先验，并开发了一种有效的参数扩展吉布斯采样器用于后验计算。使用模拟数据和一个流行病学实例对该方法进行了说明。
