• 演讲人： 王迪（美国芝加哥大学布斯商学院，博士后研究员）
• 时间：2022年06月29日 周三上午11:00
• 地点：腾讯会议 815-764-550
• 主办单位：数据科学研究中心

摘要：High-dimensional time series data appear in many scientific areas in the current data-rich environment. Analysis of such data poses new challenges to data analysts because of not only the complicated dynamic dependence between the series, but also the existence of aberrant observations, such as missing values, contaminated observations, and heavy-tailed distributions. For high-dimensional vector autoregressive (VAR) models, we introduce a unified estimation procedure that is robust to model misspecification, heavy-tailed noise contamination, and conditional heteroscedasticity. The proposed methodology enjoys both statistical optimality and computational efficiency, and can handle many popular high-dimensional models, such as sparse, reduced-rank, banded, and network-structured VAR models. With proper regularization and data truncation, the estimation convergence rates are shown to be almost optimal in the minimax sense under a bounded (2 + 2ε)-th moment condition. When ε ≥ 1, the rates of convergence match those obtained under the sub-Gaussian assumption. Consistency of the proposed estimators is also established for some ε ∈ (0,1), with minimax optimal convergence rates associated with ε. The efficacy of the pro- posed estimation methods is demonstrated by simulation and a U.S. macroeconomic example. This talk is based on the joint work with Ruey S. Tsay.

报告人简介：王迪，芝加哥大学布斯商学院博士后研究员。2020年在香港大学获得统计学博士学位，2020年10月至2022年6月于芝加哥大学布斯商学院从事博士后研究，将于2022年7月入职上海交通大学数学科学学院任职轨副教授。主要从事高维数据分析、时间序列分析和机器学习的理论与应用研究，已在Journal of the American Statistical Association，Statistica Sinica，AAAI等统计期刊与机器学习会议发表论文。

联系人：高照省（zhaoxing_gao@zju.edu.cn）

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