论文标题
加强基于Copula的组件
Boosting with copula-based components
论文作者
论文摘要
作者为二元结果提出了新的添加剂模型，其中组件是基于Copula的回归模型（Noh等，2013），并设计使该模型可以捕获潜在的复杂相互作用效果。这些模型不需要连续协变量的离散化，因此适用于许多此类协变量的问题。描述了一种拟合算法，以及用于模型选择和评估组件的有效程序。软件是在R-Package Copulaboost中提供的。数据集的仿真和插图表明该方法的预测性能要么比其他方法更好或可比。
The authors propose new additive models for binary outcomes, where the components are copula-based regression models (Noh et al, 2013), and designed such that the model may capture potentially complex interaction effects. The models do not require discretisation of continuous covariates, and are therefore suitable for problems with many such covariates. A fitting algorithm, and efficient procedures for model selection and evaluation of the components are described. Software is provided in the R-package copulaboost. Simulations and illustrations on data sets indicate that the method's predictive performance is either better than or comparable to the other methods.
