Model-free posterior inference on the area under the receiver operating characteristic curve
Model-free posterior inference on the area under the receiver operating characteristic curve
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DOI：
10.1016/j.jspi.2020.03.008
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发表时间：
2020-12-01
影响因子：
0.9
通讯作者：
Martin, Ryan
中科院分区：
文献类型：
--
作者：
Wang, Zhe;Martin, Ryan
受试者工作特征曲线（AUC）下的面积作为二元分类器性能的总结。对于AUC的推断，一个常见的建模假设是二正态性，它限制了分类器产生的分数的分布。然而，这种假设引入了无限维的滋扰参数，并且在某些机器学习设置中可能是限制性的。为了避免做出分布假设，并避免完全非参数分析的计算挑战，我们开发了一个直接和无模型的吉布斯后验分布来推断AUC。我们提出了渐近吉布斯后验集中率，和调整学习率，使相应的可信区间达到标称频率覆盖概率的策略。仿真实验和真实的数据分析表明，吉布斯后验的强大性能相比，现有的贝叶斯方法。(C)2020爱思唯尔B.V.保留所有权利。
The area under the receiver operating characteristic curve (AUC) serves as a summary of a binary classifier's performance. For inference on the AUC, a common modeling assumption is binormality, which restricts the distribution of the score produced by the classifier. However, this assumption introduces an infinite-dimensional nuisance parameter and may be restrictive in certain machine learning settings. To avoid making distributional assumptions, and to avoid the computational challenges of a fully nonparametric analysis, we develop a direct and model-free Gibbs posterior distribution for inference on the AUC. We present the asymptotic Gibbs posterior concentration rate, and a strategy for tuning the learning rate so that the corresponding credible intervals achieve the nominal frequentist coverage probability. Simulation experiments and a real data analysis demonstrate the Gibbs posterior's strong performance compared to existing Bayesian methods. (C) 2020 Elsevier B.V. All rights reserved.