论文标题
分层嵌入的贝叶斯添加剂回归树
Hierarchical Embedded Bayesian Additive Regression Trees
论文作者
论文摘要
我们提出了贝叶斯添加剂回归树的简单而强大的扩展，我们将其命名为层次结构嵌入了BART（HE-BART）。该模型允许在一组回归树的末端节点级别中包括随机效应，使He-Bart成为混合效应模型的非参数替代品，避免了用户在模型中指定随机效应的结构，同时维持了标准BART的预测和不确定性校准属性。使用模拟和现实世界的示例，我们证明了这种新扩展为许多标准混合效应模型的示例数据集提供了卓越的预测，但仍然提供了对随机效应方差的一致估计。在本文的未来版本中，我们概述了它在较大，更高级的数据集和结构中的使用。
We propose a simple yet powerful extension of Bayesian Additive Regression Trees which we name Hierarchical Embedded BART (HE-BART). The model allows for random effects to be included at the terminal node level of a set of regression trees, making HE-BART a non-parametric alternative to mixed effects models which avoids the need for the user to specify the structure of the random effects in the model, whilst maintaining the prediction and uncertainty calibration properties of standard BART. Using simulated and real-world examples, we demonstrate that this new extension yields superior predictions for many of the standard mixed effects models' example data sets, and yet still provides consistent estimates of the random effect variances. In a future version of this paper, we outline its use in larger, more advanced data sets and structures.
