应兰州大学数学与统计学院邀请，江西财经大学统计学院江河博士将于2016年12月18日至12月20日访问我校并作学术报告。
报告题目：Group regularized estimation under structural hierarchy
时 间：2016年12月19日（星期一）16：30
地 点：齐云楼911报告厅
摘 要: In high-dimensional models that involve interactions, statisticians usually favor variable selection obeying certain logical hierarchical constraints. This paper focuses on structural hierarchy which means that the existence of an interaction term implies that at least one or both associated main effects must be present. Lately this problem has attracted a lot of attentions from statisticians, but existing computational algorithms converge slow and cannot meet the challenge of big data computation. More importantly, theoretical studies of hierarchical variable selection are extremely scarce, largely due to the difficulty that multiple sparsity-promoting penalties are enforced on the same subject. This work investigates a new type of estimator based on group multi-regularization to capture various types of structural parsimony simultaneously. We present non-asymptotic results based on combined statistical and computational analysis, and reveal the minimax optimal rate. A general-purpose algorithm is developed with a theoretical guarantee of strict iterate convergence and global optimality. Simulations and real data experiments demonstrate the efficiency and efficacy of the proposed approach.
欢迎届时参加。
主讲人简介:
江河博士现就职于江西财经大学统计学院，任助理教授。2005年9月至2009年6月就读于兰州大学数学与统计学院数学基地班；2009年9月被保送至兰州大学数学与统计学院攻读概率论与数理统计硕士学位; 2011年9月至2012年5月在美国奥本大学数学与统计学院访问学习；2012年9月至2015年5月于佛罗里达州立大学取得统计学博士学位。江河的研究领域为大数据下的变量选择问题及其相关应用。发表SCI学术8篇，主持江西省教育厅基金一项。
数学与复杂系统省级重点实验室
数学与统计学院
萃英学院
2016年12月19日