“数理讲堂”2023年第52期:A General M-estimation Theory in Semi-Supervised Framework

发布时间:2023-11-28 供稿:数理与统计学院 分享至:

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

时间:1129 9:00

地点:腾讯会议:438-014-7163

主持人:姜荣教授

报告人简介:

宋珊珊博士,目前在香港中文大学统计系做博士后研究员。在此之前,她在2020年于上海财经大学取得博士学位。宋博士的研究方向包括大数据分析,半监督学习,统计机器学习。相应成果已发表在JASA,CJS,Fundamental Research等期刊。

讲座简介:

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.


上一篇:下一篇:
最新导读