Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models
Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models
复制标题
潜在回归模型中贝叶斯变量选择的完全吉布斯采样算法
DOI：
10.1111/jedm.12348
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发表时间：
2022
影响因子：
1.3
通讯作者：
Zhang Jihong
中科院分区：
文献类型：
--
作者：
Yamaguchi Kazuhiro;Zhang Jihong
本研究提出了吉布斯采样算法，用于一维双参数逻辑项目响应理论模型下的潜在回归模型中的变量选择。采用三种类型的收缩先验来获得收缩估计：双指数（即拉普拉斯）、马蹄形和马蹄+先验。在模拟和实际数据分析中将这些收缩先验与统一的先验情况进行比较。模拟研究表明，两种类型的马蹄先验比双指数先验或均匀先验具有更小的均方根误差和更短的 95% 可信区间长度。此外，马蹄铁+先验比马蹄铁先验稍微稳定一些。真实的数据示例成功证明了马蹄形和马蹄形+先验在选择数学成绩的有效预测协变量方面的效用。
This study proposed Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two‐parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double‐exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were compared to a uniform prior case in both simulation and real data analysis. The simulation study revealed that two types of horseshoe priors had a smaller root mean square errors and shorter 95% credible interval lengths than double‐exponential or uniform priors. In addition, the horseshoe+ prior was slightly more stable than the horseshoe prior. The real data example successfully proved the utility of horseshoe and horseshoe+ priors in selecting effective predictive covariates for math achievement.