时 间：2023年5月10日 15:30-17:30
地 点：理科大楼A1716
报告人：王迪上海交通大学副教授
主持人：章迎莹华东师范大学副教授
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 proposed estimation methods is demonstrated by simulation and a U.S. macroeconomic example.
报告人简介：
王迪是上海交通大学数学科学学院长聘教轨副教授，2020年博士毕业于香港大学，随后在芝加哥大学商学院从事博士后研究。它的研究方向包括时间序列、机器学习、高维数据分析等。在Annals of Statistics, JASA，JBES, Statistica Sinica等杂志上发表学术论文多篇。