报告题目：Simultaneous Inference for Nonlinear Time Series, a Sieve M-regression Approach
报 告 人： 周舟教授 浙江大学
报告时间：2026年9月10日16：00-17：00
报告地点： 伍卓群楼第一报告厅
校内联系人：韩月才 hanyc@jlu.edu.cn
报告摘要：
This talk studies simultaneous inference of conditional distributions in nonlinear time series from a sieve M-regression perspective. Existing literature on sieve M-regression has primarily focused on pointwise asymptotics, leaving the development of uncertainty quantification over the entire predictor space unexplored. We address this gap by establishing a uniform Bahadur representation for the sieve M-estimator, accommodating dependent data and a growing number of sieve basis functions. A novel high-dimensional empirical process theory is developed for temporally dependent data, and a specifically designed M-decomposition method is utilized to control high-dimensional complexities. Building on this representation, we develop a convex Gaussian approximation to characterize the asymptotic behavior of the estimator and construct valid simultaneous confidence regions (SCRs). To facilitate practical implementation, we introduce a self-convolved bootstrap algorithm that accurately approximates the distribution of the maximal deviation. Our inferential framework is supported by rigorous error bounds and validated through numerical simulations and real data applications.
报告人简介：
周舟，浙江大学求是讲席教授、博士生导师。2003年北京大学数学学士，2009年芝加哥大学统计学博士。2009—2026年于多伦多大学大学统计科学系任教，历任助理教授、终身副教授、终身正教授，并获得2021年加拿大NSERC Discovery Accelerator奖、2023年CRM-SSC奖等奖项。2026年回国加入浙江大学数据科学研究中心。主要研究方向为复杂时空数据分析、非参数方法、时频域分析、重抽样方法、变点分析等。他在统计学、计量经济学、信息论顶级期刊发表多篇论文，研究得到顶级研究机构支持。
