报告题目：Transfer learning for joint mean and variance model

报告人：宋允全

报告时间：2026年1月10日 15:30-16:30

报告地点：体育赛事直播平台 106会议室

摘要：This paper investigates the problem of variable selection in joint mean and variance models under high-dimensional settings within the transfer learning framework. The primary goal is to enhance parameter estimation and prediction accuracy for the target dataset by leveraging source datasets that share similarities with the target. First, a two-step transfer learning method based on the Lasso penalty is proposed for scenarios where the transferable source datasets are known. Second, for situations where transferable sources are unknown, an algorithm-independent, data-driven source selection method is introduced. This approach effectively distinguishes between transferable and nontransferable sources and improves prediction performance on the target dataset by utilizing the identified transferable sources. Extensive simulation studies demonstrate the superior performance of the proposed methods. Furthermore, experiments on real-world datasets highlight their practical applicability and effectiveness.

报告人简介：宋允全，博士，教授，博士生导师，中国石油大学（华东）理体育赛事直播 数据科学与统计系书记兼副主任，学校统计学科、数据科学与大数据技术专业带头人。主要从事空间网络数据统计建模、迁移学习、隐私保护及应用研究，主持数据科学领域科研项目5项，包括国家重点研发计划子课题1项、教育部人文社科基金1项、山东省自然科学基金3项。在统计学、数据挖掘、机器学习领域期刊如Statistica Sinica、Statistics and Computing、European Journal of Operational Research 、Computational Statistics & Data Analysis 、Spatial Statistics 、Spatial Economic Analysis、Statistical Analysis and Data Mining、Expert Systems With Applications等发表论文70余篇。授权国家发明专利4项，担任中国现场统计学会中国青年统计学家协会常务理事中国现场统计生存分析分会理事，中国现场统计资源与环境分会常务理事，中国现场统计高维数据分会理事，中国现场统计研究会贝叶斯统计分会理事，中国优选法统筹法与经济数学研究会数据科学分会理事、山东省大数据研究会大数据专业建设委员会理事，青岛市统计专家咨询委员会委员等。