TEDS-CQE系列专场报告会由国际计量经济学和数据科学论坛（Transdisciplinary Econometrics & Data Science Seminar）与吉林大学数量经济研究中心（Center for Quantitative Economics of Jilin University）联合举办。该系列专场报告会是2022年第十一届数量经济学国际学术会议(中国•长春)的特邀报告之一，报告会将聚焦数量经济学理论与方法的最新进展及其相关应用，邀请知名计量经济学家和统计学家分享其最新的研究成果。

In this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on GLM, and derive its ℓ1/ℓ2-estimation error bounds as well as a bound for a prediction error measure. The theoretical analysis shows that when the target and source are sufficiently close to each other, these bounds could be improved over those of the classical penalized estimator using only target data under mild conditions. When we don't know which sources to transfer, an algorithm-free transferable source detection approach is introduced to detect informative sources. The detection consistency is proved under the high-dimensional GLM transfer learning setting. We also propose an algorithm to construct confidence intervals of each coefficient component, and the corresponding theories are provided. Extensive simulations and a real-data experiment verify the effectiveness of our algorithms. We implement the proposed GLM transfer learning algorithms in a new R package glmtrans, which is available on CRAN.