In this article, we propose a novel constrained Bayesian elastic net approach for linear quantile mixed model shrinkage. A partially collapsed Gibbs sampling algorithm is developed for efficient posterior computation based on a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We demonstrate the proposed method based on simulated data and an experimental dataset from a longitudinal study of age-related macular degeneration trial. Both simulation studies and real data analysis indicate that the proposed constrained Bayesian elastic net approach is competitive with the existing methods under a variety of scenarios, such as presence of a large number of covariates and collinearity.

在本文中，我们提出了一种新颖的约束贝叶斯弹性网络方法，用于线性分位数混合模型收缩。基于改进的 Cholesky 分解用于随机效应的协方差矩阵和用于误差分布的非对称拉普拉斯分布，开发了一种部分折叠的 Gibbs 采样算法，用于有效的后验计算。我们基于模拟数据和来自年龄相关性黄斑变性试验纵向研究的实验数据集演示了所提出的方法。仿真研究和实际数据分析都表明，所提出的约束贝叶斯弹性网方法在存在大量协变量和共线性等各种情况下与现有方法具有竞争力。