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 to select the transferable sources adaptively and establish its selection consistency under regular conditions. Monte Carlo simulations as well as an empirical analysis of gene expression data demonstrate the effectiveness of the proposed procedure.

翻译：本文研究迁移学习框架下的高维分位数回归问题，在该框架中可利用可能相关的源数据集来改进仅基于目标数据的估计或预测。在已知可迁移源数据的理想情形下，提出一种基于卷积平滑的平滑两步迁移学习算法，并建立了相应估计量的L1/L2误差界。为避免纳入非信息源，我们提出自适应选取可迁移源的方法，并在正则条件下建立其选择相合性。蒙特卡洛模拟及基因表达数据的实证分析验证了所提方法的有效性。