经济与金融系学术讨论会第209期

题目：A General M-estimation Theory in Semi-Supervised Framework

演讲人：宋珊珊（同济大学数学科学学院 助理教授）

时间：2024年4月16日（周二）12:00-13:00

地点：同济大厦A楼505室

同步#腾讯会议：977532371

会议密码：738325

链接：https://meeting.tencent.com/dm/SmLxkcec824A

摘要：We study a class of general M-estimators in the semi-supervised setting, wherein the data are typically a combination of a relatively small labeled dataset and large amounts of unlabeled data. A new estimator, which efficiently uses the useful information contained in the unlabeled data, is proposed via a projection technique. We prove consistency and asymptotic normality, and provide an inference procedure based on K-fold cross-validation. The optimal weights are derived to balance the contributions of the labeled and unlabeled data. It is shown that the proposed method, by taking advantage of the unlabeled data, produces asymptotically more efficient estimation of the target parameters than the supervised counterpart. Supportive numerical evidence is shown in simulation studies. Applications are illustrated in analysis of the homeless data in Los Angeles. Supplementary materials for this article are available online.

简介：宋珊珊，同济大学数学科学学院助理教授。2020年在上海财经大学获得博士学位，之后在香港中文大学统计学系做博士后研究员，直至2024年加入同济大学。她的研究兴趣包括：大数据分布式计算、半监督学习、统计机器学习等。其成果发表或接收于JMLR、JASA等期刊。