Cho Hunyong, Jewell Nicholas P, Kosorok Michael R
Department of Biostatistics, University of North Carolina at Chapel Hill.
Department of Medical Statistics & Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine.
J Comput Graph Stat. 2022;31(2):390-402. doi: 10.1080/10618600.2021.1987253. Epub 2021 Nov 17.
We propose interval censored recursive forests (ICRF), an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator addresses the splitting bias problem of existing tree-based methods and iteratively updates survival estimates in a self-consistent manner. Consistent splitting rules are developed for interval censored data, convergence is monitored using out-of-bag samples, and kernel-smoothing is applied. The ICRF is uniformly consistent and displays high prediction accuracy in both simulations and applications to avalanche and national mortality data. An R package icrf is available on CRAN and Supplementary Materials for this article are available online.
我们提出了区间删失递归森林（ICRF），这是一种用于区间删失生存数据的迭代树集成方法。这种非参数回归估计器解决了现有基于树的方法的分裂偏差问题，并以自洽的方式迭代更新生存估计。针对区间删失数据开发了一致的分裂规则，使用袋外样本监测收敛情况，并应用核平滑。ICRF在模拟以及对雪崩和国家死亡率数据的应用中具有一致的一致性且显示出高预测准确性。可在CRAN上获取R包icrf，本文的补充材料可在线获取。
