论文标题
审查的分位数回归森林
Censored Quantile Regression Forest
论文作者
论文摘要
随机森林是强大的非参数回归方法，但在存在随机审查的观察结果的情况下，它们的使用受到了严重限制，并且由于偏见而导致的天真应用可以表现出较差的预测性能。基于随机森林的局部自适应表示，我们为随机审查的回归分位模型开发了其回归调整。回归调整基于一个新的估计方程，该方程适用于检查，并且只要数据不显示审查，就会导致分数分数。提出的名为{\ it审查分位数回归森林}的程序使我们能够在没有任何参数建模假设的情况下估算事件的分位数。我们在温和的模型规范下建立了它的一致性。数值研究展示了所提出的程序的明显优势。
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on a new estimating equation that adapts to censoring and leads to quantile score whenever the data do not exhibit censoring. The proposed procedure named {\it censored quantile regression forest}, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.
