报告时间🏌🏼‍♀️：2023年5月15日（星期一）下午14:00-16:00

报告地点：沙河校区🎡👰🏼‍♀️，K8凯发1号楼102

报告人：周舟🔩，多伦多大学♢，教授

报告摘要🌂：In this talk I shall introduce some statistical inference tools for high dimensional panel functional time series and functional time series regression. A new concept of physical dependent processes in the space of square integrable functions will be discussed, which adopts the idea of basis decomposition of functional data in these spaces. Gaussian and multiplier bootstrap approximations for sums of high dimensional functional time series will be derived. These results have numerous important statistical consequences. Exemplarily, we consider the development of joint simultaneous confidence bands for high dimensional mean functions of panel functional time series and slopes of time series functional linear regression, as well as the construction of tests for the hypotheses that the mean functions in the panel dimension are parallel.

报告人简介：周舟👨‍🦯‍➡️⚙️，多伦多大学统计系教授🏸。主要研究领域为时间序列分析和非参数统计😉，周教授在统计学顶级期刊上发表了31篇论文🦑，其中Annals of Statistics（6次）、JRSSB（5次）和JASA（2次）。由于对统计学的重要贡献，他在2021年获得了NSERC-Discovery Accelerator Supplement Award，并在2023年获得了CRM-SCC奖，该奖项由加拿大统计协会每年颁发，用于奖励近15年来在统计学领域做出突出贡献的杰出学者，该奖项肯定了周教授在时间序列分析和非参数统计方面的贡献，包括非平稳时间序列、非线性时间序列、时域频域分析、高斯近似、重抽样方法和复杂相关数据推断，以及非平稳数据的稳健突变点检测等。