时 间：2022年11月9日 9:30-11:00
地 点：腾讯会议ID：792-864-714
报告人：乔兴昊 副教授
主持人：彭梦姣 助理教授
摘 要：
We propose a two-step procedure to model and predict high-dimensional functional time series, where the number p of function-valued variables is large in relation to the number n of serially dependent observations. Our first segmentation step uses the eigenanalysis of a positive definite matrix to look for linear transformation of original high-dimensional functional time series such that the transformed curve series can be segmented into multiple groups of low-dimensional subseries, and the subseries in different groups are uncorrelated both contemporaneously and serially. Modelling each low-dimensional subseries separately will not lose the overall linear dynamical information, and at the same time, can avoid the overparametrization issue arisen from directly modelling original high-dimensional curve series. Our second dimension-reduction step estimates the finite-dimensional dynamical structure for each group of the transformed curve series that converts the problem of modelling low-dimensional functional time series to that of modelling vector time series. Efficient strategies can be implemented to predict vector time series groupwisely, which can then be converted back to predict groups of transformed curve subseries and finally original functional time series. We investigate the theoretical properties of our proposal when p diverges at an exponential rate of n. The superior finite-sample performance of the proposed methods is illustrated through both extensive simulations and three real datasets.
报告人简介：
乔兴昊在美国南加州大学马绍尔商学院获得商业统计学博士，现任英国伦敦政治经济学院统计系长聘副教授。研究方向为函数型数据分析，时间序列分析，高维统计推断和贝叶斯非参数等。多篇研究成果发表在Journal of the American Statistical Association, Biometrika, Journal of Econometrics, Journal of Business and Economic Statistics等统计学与计量经济学国际权威学术期刊上。