标题： 基于测试的贝叶斯众数回归变量选择 显示英文标题

标题： Testing-driven Variable Selection in Bayesian Modal Regression

摘要： 我们提出了一种在模态回归框架下的贝叶斯变量选择方法，用于处理重尾响应。采用一种高效的期望最大化算法以加快参数估计。构建了一个检验统计量，以利用模型误差分布的形状来有效区分有信息的协变量和不重要的协变量。通过模拟，我们展示了所提出方法在存在非高斯模型误差的情况下识别重要协变量的有效性。最后，我们将所提出的方法应用于分析遗传学和表观遗传学研究中的两个数据集。 显示英文摘要

摘要： We propose a Bayesian variable selection method in the framework of modal regression for heavy-tailed responses. An efficient expectation-maximization algorithm is employed to expedite parameter estimation. A test statistic is constructed to exploit the shape of the model error distribution to effectively separate informative covariates from unimportant ones. Through simulations, we demonstrate and evaluate the efficacy of the proposed method in identifying important covariates in the presence of non-Gaussian model errors. Finally, we apply the proposed method to analyze two datasets arising in genetic and epigenetic studies.