Estimating quantiles of an outcome conditional on covariates is of fundamental interest in statistics with broad application in probabilistic prediction and forecasting. We propose an ensemble method for conditional quantile estimation, Quantile Super Learning, that combines predictions from multiple candidate algorithms based on their empirical performance measured with respect to a cross-validated empirical risk of the quantile loss function. We present theoretical guarantees for both iid and online data scenarios. The performance of our approach for quantile estimation and in forming prediction intervals is tested in simulation studies. Two case studies related to solar energy are used to illustrate Quantile Super Learning: in an iid setting, we predict the physical properties of perovskite materials for photovoltaic cells, and in an online setting we forecast ground solar irradiance based on output from dynamic weather ensemble models.

翻译：基于协变量条件的结果变量分位数估计是统计学中的基础问题，在概率预测与预报领域具有广泛的应用。本文提出一种用于条件分位数估计的集成方法——分位数超学习，该方法结合多个候选算法的预测结果，其集成权重由基于分位数损失函数的交叉验证经验风险所衡量的实证表现决定。我们为独立同分布数据和在线数据场景分别提供了理论保证。通过模拟研究验证了该方法在分位数估计及预测区间构建中的性能。两项与太阳能相关的案例研究展示了分位数超学习的应用：在独立同分布场景中，我们预测光伏电池钙钛矿材料的物理性质；在在线场景中，我们基于动态天气集合模型的输出预测地面太阳辐照度。