针对重尾数据，提出贝叶斯模态回归变量选择新方法。
Testing-driven Variable Selection in Bayesian Modal Regression
- 基于模态回归框架，用检验统计量区分重要与无关变量。
- 在非高斯误差下，能有效识别关键协变量。
- 适用于遗传与表观遗传数据分析，适合生物统计研究者。
针对重尾响应变量，我们提出一种贝叶斯变量选择方法，基于模态回归框架。采用高效的期望-最大化算法加速参数估计，并构建检验统计量，利用模型残差分布形状，有效区分重要协变量与无关变量。通过模拟实验，验证了该方法在非高斯误差条件下识别关键协变量的有效性。最后，将该方法应用于两个来自遗传学与表观遗传学研究的真实数据集，展示了其实际应用价值。
原文摘要 · Abstract (English)
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.
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