报告人：杨玥含（中央财经大学）

时间：2024年03月08日 13:00-

地址：理科楼LA103

摘要：In this talk，we discuss the target model with the help of auxiliary models from different but possibly related groups. Inspired by transfer learning, we propose a method called joint estimation transferred from strata (JETS). To obtain a sparse solution, JETS constructs a penalized framework combining a term that penalizes the target model and an additional term that penalizes the differences between auxiliary models and the target model. In this way, JETS overcomes the challenge caused by the limited samples in high-dimensional study, and obtains stable and accurate estimates regardless of whether auxiliary samples contain noisy information. We demonstrate that this method enjoys the computational advantage of the traditional methods such as the lasso. During simulations and applications, the proposed method is compared with several existing methods and JETS outperforms others.

简介：杨玥含，教授，中央财经大学龙马青年学者，主要从事多重结构数据建模，因果推断，迁移学习，资产配置等研究，主持承担国家自然科学基金青年基金项目、面上项目各一项，作为通信作者在统计学四大期刊JASA，Biometrika，人工智能顶级期刊PR，ESWA，KBS，中国科学中英文版，数学年刊B辑等中外期刊发表论文近四十篇。重庆大学2005级统计学专业本科毕业，北京大学2009级应用数学专业博士毕业，曾获北京国际数学中心研究生奖学金（2014，共9人），先后访问过美国密西根大学、斯坦福大学、澳大利亚国立大学等高校，受邀在国内外学术会议上做了十余次邀请报告并担任分组主席。

邀请人：统计与精算学系

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