In statistics, time-to-event analysis methods traditionally focus on the estimation of hazards. In recent years, machine learning methods have been proposed to directly predict the event times. We propose a method based on vine copula models to make point and interval predictions for a right-censored response variable given mixed discrete-continuous explanatory variables. Extensive experiments on simulated and real datasets show that our proposed vine copula approach provides a decent approximation to other time-to-event analysis models including Cox proportional hazards and Accelerate Failure Time models. When the Cox proportional hazards or Accelerate Failure Time assumptions do not hold, predictions based on vine copulas can significantly outperform other models, depending on the shape of the conditional quantile functions. This shows the flexibility of our proposed vine copula approach for general time-to-event datasets.
翻译:在统计中,时间对活动的分析方法传统上侧重于灾害的估计。近年来,提出了直接预测事件时间的机器学习方法。我们提议了一种基于葡萄干模型的方法,以根据分辨和间隔预测右审查反应变量,因为有多种离散和连续的解释变量。关于模拟和真实数据集的广泛实验表明,我们提议的葡萄干方法为其他时间对活动分析模型提供了体面的近似,包括Cox成比例的危险和加速故障时间模型。当Cox成比例的危险或加速故障时间假设不起作用时,以葡萄干草为基础的预测可以大大超过其他模型,视有条件的孔函数的形状而定。这显示了我们提议的葡萄干对一般时间对活动数据集的灵活性。