We propose the interval censored recursive forests (ICRF) which is an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator makes the best use of censored information by iteratively updating the survival estimate, and can be viewed as a self-consistent estimator with convergence monitored using out-of-bag samples. Splitting rules optimized for interval censored data are developed and kernel-smoothing is applied. The ICRF displays the highest prediction accuracy among competing nonparametric methods in most of the simulations and in an applied example to avalanche data. An R package icrf is available for implementation.

翻译：我们建议使用隔间审查再生森林(ICRF),这是隔间审查生存数据的迭代树群混合方法。这个非参数回归估计值通过迭代更新生存估计数据,最佳地利用受审查信息,并可以被视为自成一体的估算器,使用包外样本监测趋同情况。为隔间审查数据制定了优化的分解规则,并采用了内核吸附法。ICRF在大多数模拟和雪崩数据应用实例中,展示了相互竞争的非对称方法之间的最高预测准确性。R包icrf可用于实施。