We propose a random forest based estimation procedure for Quantile-Oriented Sensitivity Analysis—QOSA. In order to be efficient, a cross-validation step on the leaf size of trees is required. Our full estimation procedure is tested on both simulated data and a real dataset. Our estimators use either the bootstrap samples or the original sample in the estimation. Also, they are either based on a quantile plug-in procedure (the R-estimators) or on a direct minimization (the Q-estimators). This leads to 8 different estimators which are compared on simulations. From these simulations, it seems that the estimation method based on a direct minimization is better than the one plugging the quantile. This is a significant result because the method with direct minimization requires only one sample and could therefore be preferred.

我们提出了一种基于随机森林的面向分位数敏感性分析的估计程序——QOSA。为了提高效率，需要对树的叶子大小进行交叉验证步骤。我们的完整估计程序在模拟数据和真实数据集上进行了测试。我们的估计器在估计中使用引导样本或原始样本。此外，它们要么基于分位数插件程序（R估计器），要么基于直接最小化（Q估计器）。这导致了 8 个不同的估计器在模拟中进行比较。从这些模拟来看，基于直接最小化的估计方法似乎比插入分位数的方法更好。这是一个重要的结果，因为直接最小化方法仅需要一个样本，因此可能是首选。