This paper studies the high-dimensional quantile regression problem under the transfer learning framework, where possibly related source datasets are available to make improvements on the estimation or prediction based solely on the target data. In the oracle case with known transferable sources, a smoothed two-step transfer learning algorithm based on convolution smoothing is proposed and the L1/L2 estimation error bounds of the corresponding estimator are also established. To avoid including non-informative sources, we propose a clustering-based algorithm to select the transferable sources adaptively and establish its selection consistency under regular conditions; we also provide an alternative model averaging procedure, of which the optimality of the excess risk is proved. Monte Carlo simulations as well as an empirical analysis of gene expression data demonstrate the effectiveness of the proposed procedure.

翻译：本文研究了转让学习框架下的高维四分回归问题,可能存在相关的源数据集,以便仅根据目标数据改进估计或预测;在已知可转移源的神器案例中,提出了基于平稳平滑的平滑平滑的平滑两步转移学习算法,并确定了相应的估计估计数字L1/L2的估计误差界限;为避免包括非信息源,我们建议采用基于集群的算法,以适应性方式选择可转移源,并在正常条件下确立其选择的一致性;我们还提供了另一种平均模式,其中证明超重风险的最佳性。蒙特卡洛模拟以及对基因表达数据的经验分析显示了拟议程序的有效性。