Uncertainty quantification for honest regression trees
Uncertainty quantification for honest regression trees
复制标题
诚实回归树的不确定性量化
DOI：
10.1016/j.csda.2021.107377
复制
发表时间：
2022
影响因子：
1.8
通讯作者：
Lee, Thomas C.M.
中科院分区：
文献类型：
--
作者：
Wu, Suofei;Hannig, Jan;Lee, Thomas C.M.
提出了一种新的方法来量化由诚实随机森林产生的估计和预测的不确定性。这种新方法是基于广义的基准方法，并提供了一个基准密度函数，衡量每一个单一的诚实的树是真正的模型的可能性。利用这样的密度函数，可以获得估计和预测，以及它们的置信/预测区间。与几个国家的最先进的方法的数值比较，并通过应用到几个真实的数据集所示的方法的有前途的经验性质。最后，所提出的方法在理论上支持的渐近保证。
A new method is developed for quantifying the uncertainties of the estimates and predictions produced by honest random forests. This new method is based on the generalized fiducial methodology, and provides a fiducial density function that measures how likely each single honest tree is the true model. With such a density function, estimates and predictions, as well as their confidence/prediction intervals, can be obtained. The promising empirical properties of the proposed method are demonstrated by numerical comparisons with several state-of-the-art methods, and by applications to a few real data sets. Lastly, the proposed method is theoretically backed up by an asymptotic guarantee.
影响因子：
2.5
作者：
Qi Gao;Randy C. S. Lai;Thomas C.M. Lee;Yao Li
通讯作者：
Qi Gao;Randy C. S. Lai;Thomas C.M. Lee;Yao Li
BART 的半参数推理
DOI：
--
发表时间：
2020
期刊：
International Conference on Machine Learning
影响因子：
--
作者：
V. Ročková
通讯作者：
V. Ročková
影响因子：
1.1
作者：
Wu, Suofei;Hannig, Jan;Lee, Thomas C.
通讯作者：
Lee, Thomas C.