2021/11/13 by Shenyi Pan, Harry Joe, Pan, Shenyi +1 · 1 citation
Decision Sciences · #FOS: Computer and information sciences #Forecasting Techniques and Applications #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2111.07179
openalex publication_date 2021/11/13 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.