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来自交叉验证的考克斯模型测试错误的置信区间
Min Woo Sun1, Robert Tibshirani1,2
1Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
Statistics in medicine
|August 15, 2023
概括
标准交叉验证 (CV) 可以低估模型测试错误,因为相关的估计. 嵌套的CV改善了统计学习模型的置信区间覆盖率,包括Cox比例危险模型.
科学领域:
- 统计学学习 统计学学习
- 机器学习模型评估评估
- 对生存分析的分析.
背景情况:
- 交叉验证 (CV) 是估计模型测试误差的标准方法.
- 由于相关的错误估计,标准CV可能会产生不充分覆盖的置信区间.
- 这种低估是因为数据样本用于培训和测试.
研究的目的:
- 为解决模型评估标准交叉验证的覆盖范围问题.
- 将嵌套交叉验证方法推广为改进的差异估计.
- 探索使用嵌套的CV.Cox比例危险模型的测试误差估计.
主要方法:
- 实施嵌套交叉验证 (嵌套CV) 以估计预测错误.
- 计算预测错误的平均平方误差,以计算相关性.
- 将嵌套CV应用到Cox比例危险模型框架中.
主要成果:
- 与标准CV相比,嵌套CV显示出更高的置信区间覆盖率.
- 拟议的方法减轻了测试误差估计差异低估的情况.
- 该研究探讨了考克斯模型背景下的各种测试错误指标.
结论:
- 嵌套CV提供了一种更可靠的方法来评估模型性能和不确定性.
- 这种技术在生存分析中的Cox比例危险模型中尤为有价值.
- 在CV估计中考虑相关性对于准确的模型评估至关重要.
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