统计与数据科学系系列学术报告之三百八十八-三百八十九

统计与数据科学系系列学术报告之三百八十八

时间：2023年04月03日（周一）14:00-15:00

主持人：复旦大学管理学院统计与数据科学系沈娟副教授

地点：史带楼205室

报告人：Professor Annie Qu

Department of Statistics, University of California Irvine

题目：Optimal Individualized Treatment Rule For Combination Treatments Under Budget Constraints

摘要：The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precision medicine, personalized education, and personalized marketing. Existing ITR estimation methods mainly adopt one of two or more treatments. However, a combination of multiple treatments could be more powerful in various areas. In this talk, we propose a novel double-encoder framework to estimate the individualized treatment rule for combination treatments. The proposed method incorporates the interaction effects among different treatments and utilizes correlations among different combinations. In theory, we show that the proposed method achieves a faster convergence rate of the value reduction bound in terms of the number of treatments. Our simulation studies show that the proposed method outperforms the existing ITR estimation in various settings. We also demonstrate the superior performance of the proposed method in a real data application that recommends optimal combination treatments for Type-2 diabetes patients.

个人简介：Annie Qu, Chancellor’s Professor, Department of Statistics, University of California Irvine. Dr. Qu’s research focuses on solving fundamental issues regarding unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms on text sentiment analysis, automatic tagging and summarization, recommender systems, tensor imaging data and network data analyses for complex heterogeneous data, and achieving the extraction of essential information from large volume high-dimensional data. Her research has impacts in many different fields such as biomedical studies, genomic research, public health research, and social and political sciences. She was a recipient of the NSF Career award in 2004-2009, and is a Fellow of the Institute of Mathematical Statistics and a Fellow of the American Statistical Association.

统计与数据科学系系列学术报告之三百八十九

时间：2023年04月06日（周四）16:00-17:00

主持人：复旦大学管理学院统计与数据科学系张新生教授

地点：史带楼302室

报告人：Professor Zhou Wang

Department of Statistics and Applied Probability at the National University of Singapore

题目：Extreme eigenvalues of sample covariance matrices under generalized elliptical models

摘要：I will discuss all kinds of limiting distributions of leading eigenvalues of large dimensional sample covariance matrices when the observation matrix is from elliptical models. （This work is joint with Ding Xiucai, Xie Jiahui and Yu Long）

个人简介：Professor Zhou Wang is mainly engaged in the theoretical and applied research of statistics, especially in the research of high-dimensional data estimation, high-dimensional data testing, data dimension reduction and large-dimensional data stochastic matrix. More than 80 high-level articles have been published, many of which are published in top journals in the field of statistics and probability science, such as: Annals of Statistics, Journal of American Statistical Association, Biometrika, Annals of Probability, Probability Theory and Related Fields, Annals of Applied Probability. Some articles have been cited by many scholars. Professor Zhou Wang has presided over many projects of the National Natural Science Foundation of Singapore and has been invited to make conference reports and invitation reports at many international conferences. He is now the editor-in-chief of RMTA, IMS Fellow.