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在共变量转移下对预测集进行双倍可靠的校准
Yachong Yang1, Arun Kumar Kuchibhotla2, Eric Tchetgen Tchetgen1
1Department of Statistics & Data Science, University of Pennsylvania, Philadelphia, PA, USA.
Journal of the Royal Statistical Society. Series B, Statistical methodology
|September 16, 2024
概括
我们引入了一种适合性预测的新框架,以改善不确定性量化. 这种方法确保了可靠的预测区域,即使与共变量转移,提高数据分析的准确性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 因果推理因果推理
背景情况:
- 符合性预测为缺少数据和因果推理提供解决方案.
- 现有的方法缺乏使用现代半参数理论有效量化不确定性.
- 适应性地计算共变量转移的数据对于可靠的预测至关重要.
研究的目的:
- 为在共变量转移下对精确校准的预测区域开发一个一般框架.
- 为了利用半参数效率理论来改善不确定性量化.
- 为了应对培训和测试数据之间的分配转移的挑战.
主要方法:
- 提出一个基于有效影响功能的框架.
- 构建具有数据适应性的预测区域.
- 使用一种同变量转移假设,类似于随机失踪.
主要成果:
- 实现对未观察到的测试结果进行精确校准的预测区域.
- 证明了对共变分布变化的稳定性.
- 在不影响准确性的情况下保持覆盖率保证.
结论:
- 拟议的框架增强了合规预测中的不确定性量化.
- 它提供可靠的预测区域在共变量转移.
- 这种方法为处理数据分析中的分布转移提供了显著的进步.
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