English(EN) Controlling the False Discovery Rate via Model-Agnostic Importance Measures

新的MAS方法控制科学研究中的错误发现率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员引入了一种名为模型无关选择（MAS）的新方法，利用agent-X重要性来改进科学发现中的协变量选择。该方法旨在控制错误发现率（FDR），而无需对协变量分布做出特定假设，这与传统方法不同。MAS采用样本分割策略和一种新颖的变量重要性度量，该度量通过比较原始和修改后的协变量分布上的预测性能来定义。该方法包括FDR控制的理论保证，并在模拟和实际分析中展示了具有竞争力的性能。 AI

排序理由该集群包含一篇详细介绍新统计学方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

agent-X importance

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arXiv stat.ML TIER_1 English(EN) · Mengtao Wen, Haojie Ren, Changliang Zou · 2026-10-16 04:00

通过模型无关重要性度量控制错误发现率

arXiv:2610.21360v1 Announce Type: cross Abstract: Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models. Traditional variable selection methods often rely on specif…

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通过模型无关重要性度量控制错误发现率

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