标题： 信号自适应变量选择器用于马蹄形先验 显示英文标题

标题： Signal Adaptive Variable Selector for the Horseshoe Prior

摘要： 在本文中，我们提出了一种简单的方法，使用连续收缩先验（如流行的马蹄先验）作为模型拟合后的变量选择方法。 所提出的信号自适应变量选择器（SAVS）方法对点估计（如后验均值）进行后处理，将变量分为信号和零假设。 该方法完全自动化，不需要指定任何调参。 我们进行了一项全面的模拟研究，以比较所提出的SAVS方法与经典惩罚程序和贝叶斯模型选择程序的性能。 发现SAVS在所有考虑的设置中表现非常有竞争力，并且特别对相关设计具有鲁棒性。 我们还将SAVS应用于一个包含超过20,000个协变量的基因组数据集，以说明其可扩展性。 显示英文摘要

摘要： In this article, we propose a simple method to perform variable selection as a post model-fitting exercise using continuous shrinkage priors such as the popular horseshoe prior. The proposed Signal Adaptive Variable Selector (SAVS) approach post-processes a point estimate such as the posterior mean to group the variables into signals and nulls. The approach is completely automated and does not require specification of any tuning parameters. We carried out a comprehensive simulation study to compare the performance of the proposed SAVS approach to frequentist penalization procedures and Bayesian model selection procedures. SAVS was found to be highly competitive across all the settings considered, and was particularly found to be robust to correlated designs. We also applied SAVS to a genomic dataset with more than 20,000 covariates to illustrate its scalability.