论文标题
纵向计数数据的贝叶斯分位数回归
Bayesian Quantile Regression for Longitudinal Count Data
论文作者
论文摘要
这项工作引入了贝叶斯分位回归建模框架，用于分析纵向计数数据。在此模型中，响应变量不是连续的，因此纳入了计数的人工平滑。贝叶斯实施利用了响应变量的不对称拉普拉斯分布的正常指数混合物表示。得出有效的Gibbs采样算法，以将模型拟合到数据。通过仿真研究说明了该模型，并在神经病学绘制的应用中实施。模型比较证明了所提出的模型的实际实用性。
This work introduces Bayesian quantile regression modeling framework for the analysis of longitudinal count data. In this model, the response variable is not continuous and hence an artificial smoothing of counts is incorporated. The Bayesian implementation utilizes the normal-exponential mixture representation of the asymmetric Laplace distribution for the response variable. An efficient Gibbs sampling algorithm is derived for fitting the model to the data. The model is illustrated through simulation studies and implemented in an application drawn from neurology. Model comparison demonstrates the practical utility of the proposed model.
