主题：An autocovariance-based learning framework for high-dimensional functional time series
主讲人：常晋源教授
主持人：姜云卢
地点：腾讯会议(ID：867-808-303，密码：10956)
会议时间：2021年12月21日上午10:00-11:00
摘要
Many scientific and economic applications involve the statistical learning of high-dimensional functional time series, where the number of functional variables can be comparable to, or even larger than, the number of serially dependent functional observations. In this paper, we model observed functional time series, which are subject to errors in the sense that each functional datum arises as the sum of two uncorrelated components, one dynamic and one white noise. Motivated from the fact that the autocovariance function of observed functional time series automatically filters out the noise term, we propose a three-step procedure by first performing autocovariance-based dimension reduction and then formulating a novel autocovariance-based block regularized minimum distance estimation framework to produce block sparse estimates, from which we can finally obtain functional sparse estimates. We investigate theoretical properties of relevant estimated terms under such autocovariance-based learning framework. Finally, we illustrate the proposed estimation procedure using applications of three sparse high-dimensional functional time series models. With derived theoretical results, we study convergence properties of the associated estimators. We demonstrate via simulated and real datasets that our proposed estimators significantly outperform the competitors.
主讲人简介
常晋源 西南财经大学数据科学与商业智能联合实验室执行主任、光华特聘教授、博士生导师、四川省特聘专家、四川省统计专家咨询委员会委员，主要从事超高维数据分析相关领域的研究，正担任统计学和计量经济学国际顶级学术期刊Journal of the Royal Statistical Society Series B和Journal of Business & Economic Statistics的副主编。
