论文标题
通过卷积平滑进行高维分位数回归的转移学习
Transfer Learning for High-dimensional Quantile Regression via Convolution Smoothing
论文作者
论文摘要
本文研究了转移学习框架下的高维分位数回归问题，其中可能相关的源数据集可用于改进仅基于目标数据的估计或预测。在具有已知可转移源的Oracle情况下，提出了基于卷积平滑的平滑两步传输学习算法，并还建立了相应估计器的L1/L2估计误差界。为了避免包括非信息源，我们建议在规则条件下自适应地选择可转移的来源并确定其选择一致性。蒙特卡洛模拟以及基因表达数据的经验分析证明了所提出的程序的有效性。
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.
