Discretized conformal prediction for efficient distribution‐free inference
Discretized conformal prediction for efficient distribution‐free inference
复制标题
用于高效分布的离散共形预测——自由推理
DOI：
10.1002/sta4.173
复制
发表时间：
2018
期刊：
影响因子：
1.7
通讯作者：
Barber, Rina Foygel
中科院分区：
文献类型：
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作者：
Chen, Wenyu;Chun, Kelli‐Jean;Barber, Rina Foygel
在没有已知真实基础模型的回归问题中，保形预测方法使预测区间能够在不对基础数据的分布进行任何假设的情况下构建，除了假设训练和测试数据是可交换的。然而，这些方法都有很大的计算成本，而且要想准确地执行，回归算法需要无限次地拟合。在实践中，共形预测方法是通过简单地考虑响应变量的精细间隔值的有限网格来运行的。本文开发了离散化共形预测算法，保证以期望的概率覆盖目标值，并在计算成本和预测精度之间进行权衡。版权所有© 2018约翰威利父子有限公司
In regression problems where there is no known true underlying model, conformal prediction methods enable prediction intervals to be constructed without any assumptions on the distribution of the underlying data, except that the training and test data are assumed to be exchangeable. However, these methods bear a heavy computational cost—and, to be carried out exactly, the regression algorithm would need to be fitted infinitely many times. In practice, the conformal prediction method is run by simply considering only a finite grid of finely spaced values for the response variable. This paper develops discretized conformal prediction algorithms that are guaranteed to cover the target value with the desired probability and that offer a trade‐off between computational cost and prediction accuracy. Copyright © 2018 John Wiley & Sons, Ltd.
DOI：
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发表时间：
2014
期刊：
--
影响因子：
--
作者：
Burnaev E
通讯作者：
Burnaev E