讲座题目：Detection of Multiple Structural Breaks in Large Covariance Matrices

主 讲 人：约克大学李德柜教授

讲座时间：2019年4月4日（周四）13：40-14：40

讲座地点：6号学院楼402会议室

主办单位：数据科学学院

摘要：

This paper studies multiple structural breaks in large covariance matrices of high-dimensional time series variables satisfying the approximate factor model structure. The breaks in the second-order structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation and Wild Sparsified Binary Segmentation are used to estimate the breaks in the common and idiosyncratic error components, respectively. Under some mild conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated change points. Some numerical studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches.

主讲人简介：

李德柜教授2008年于浙江大学数学系取得理学博士学位，师从林正炎教授。随后在澳大利亚阿德莱德大学，莫纳什大学从事博士后研究，曾获2011年度澳大利亚优秀青年研究奖。现为约克大学数学系教授。李德柜教授研究兴趣集中在时间序列，半参数与非参数统计，面板数据，目前已发表论文42篇，其中15篇发表在Journal of Econometric, Annals of Statistics, Journal of the American Statistical Association, Journal of Business and Economic Statistics, Econometric Theory.他目前是Econometric and Statistics的副主编。

欢迎感兴趣的师生积极参加！