English(EN) Subdata Selection for Measurement-Constrained Linear Regression under Model Misspecification

新的ELOS方法改进了误设线性回归的数据选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一个新的线性回归数据子集选择框架，专门用于获取响应变量成本高昂或困难的情况。这种称为预期损失最优选择（ELOS）的方法侧重于预测风险，即使在模型误设的情况下也保持有效。ELOS仅使用协变量信息进行选择，使其适用于统计效率至关重要的测量约束环境。 AI

影响这种新的数据选择方法可以提高机器学习模型的效率和鲁棒性，尤其是在数据有限或成本高昂的情况下。

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arXiv stat.ML TIER_1 English(EN) · Yundi Kong, Min Yang, Ping-Shou Zhong · 2026-10-15 04:00

模型误设下测量约束线性回归的子数据选择

arXiv:2610.19351v1 Announce Type: cross Abstract: In many modern applications, obtaining response variables is costly, time-consuming, or technically challenging, making it impractical to measure outcomes for all available units. In such settings, covariates may be available for …