关于AUC变异估计偏差的偏差
1Department of Radiology, Johns Hopkins University, MD, USA.
Pattern recognition letters
|January 8, 2024
概括
德龙和其他人. 估计ROC曲线下面积 (AUC) 的方差的方法总是偏向正的. 这种偏差虽然在大样本中很小,但可以影响较小样本大小的统计分析.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 接收器运行特征 (ROC) 曲线 (AUC) 下的面积是评估二进制分类器的一个关键指标.
- 估计AUC变化率,包括方差和协差,对于假设测试和置信区间至关重要.
- 德龙和其他人. 该方法是基于曼·惠特尼U统计的AUC方差估计的广泛使用的方法.
研究的目的:
- 分析DeLong等人对偏差的偏差属性. AUC (协同变异) 估计的方法.
- 为了数学证明DeLong等人. (协同) 差异估计器总是具有积极偏差.
- 为了研究样本大小对DeLong等的偏差的影响. 一个估计者.一个估计者.
主要方法:
- 德隆等人提出的 (共同) 差异估计器的理论分析.
- 从AUC内核构建一个随机变量来表示偏差.
- 数学证明,预期和真实共变率之间的差异是一个正半定义矩阵.
主要成果:
- 在DeLong等人中的 (共同) 差异估计. 这种方法被证明总是有积极的偏见.
- 偏差对于小样本大小来说是不可忽视的,但随着样本大小的增加,偏差会减少.
- 这种偏差的特点是估计和真实协差之间的正半定义差异矩阵.
结论:
- 德龙和其他人. 对AUC变异估计的方法表现出固有的正偏差.
- 这种偏差可能会影响统计推理,特别是在数据有限的场景中.
- 探索替代AUC差异估计方法可能是必要的,以减轻这种偏差.
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