论文标题
贝叶斯线性回归的可变融合通过尖峰和slab先验
Variable fusion for Bayesian linear regression via spike-and-slab priors
论文作者
论文摘要
在线性回归模型中，系数的融合用于识别与响应相似关系的预测变量。这称为变量融合。本文在贝叶斯线性回归模型方面提出了一种新型的可变融合方法。我们专注于基于Spike and Slab先验方法的层次结构贝叶斯模型。 Spike and-Slab先验是为执行可变融合而定制的。为了获得参数的估计值，我们为参数开发了一个Gibbs采样器。仿真研究和实际数据分析表明，我们提出的方法比以前的方法更好。
In linear regression models, fusion of coefficients is used to identify predictors having similar relationships with a response. This is called variable fusion. This paper presents a novel variable fusion method in terms of Bayesian linear regression models. We focus on hierarchical Bayesian models based on a spike-and-slab prior approach. A spike-and-slab prior is tailored to perform variable fusion. To obtain estimates of the parameters, we develop a Gibbs sampler for the parameters. Simulation studies and a real data analysis show that our proposed method achieves better performance than previous methods.
