主题：Supervised Factor Modeling for High-Dimensional Linear Time Series
主讲人：李国栋 香港大学
主持人：王国长 88858cc永利官网
时间：2024年11月22日（周五）下午16:30-17:30
地点：88858cc永利官网石牌校区88858cc永利官网大楼（中惠楼）306室
摘要
Motivated by Tucker tensor decomposition, this paper imposes low-rank structures to the column and row spaces of coefficient matrices in a multivariate infinite-order vector autoregression (VAR), which leads to a supervised factor model with two factor modelings being conducted to responses and predictors simultaneously. Interestingly, the stationarity condition implies an intrinsic weak group sparsity mechanism of infinite-order VAR, and hence a rank-constrained group Lasso estimation is considered for high-dimensional linear time series. Its non-asymptotic properties are discussed thoughtfully by balancing the estimation, approximation and truncation errors. Moreover, an alternating gradient descent algorithm with thresholding is designed to search for high-dimensional estimates, and its theoretical justifications, including statistical and convergence analysis, are also provided. Theoretical and computational properties of the proposed methodology are verified by simulation experiments, and the advantages over existing methods are demonstrated by two real examples.
主讲人简介
李国栋，本科和硕士毕业于北大数学学院，2007年于香港大学统计精算系获得统计学博士，随后在南洋理工大学任助理教授。现任香港大学统计精算系教授。主要研究方向包括时间序列分析，分位数回归，高维统计数据分析和机器学习。目前发表学术论文 60余篇，其中10余篇发表在统计学4大顶级期刊，以及机器学习的顶级会议上。
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校对| 王国长
责编| 彭 毅
初审| 姜云卢
终审发布| 何凌云
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