时 间：2023年4月19日（周三）10：00-11：00
地 点: 腾讯会议：823-714-212 ；密码：4321
报告人：冯龙 南开大学副教授
主持人：刘玉坤 教授
摘 要：
High frequency regression has received more and more attention recent years. This is the fifirst paper about detecting the change points in the beta process of high frequency regression. As an intermediate modelling approach between the constant beta and continuous beta process, this paper employs the piece-wise constant process to model the time-varying beta, which can ensure both parsimony and flexibility of the high frequency regression model at the same time. By extending the idea of self-normalization to the observations with timevarying covariance structure, we have developed the testing and estimation methodologies for the scenario of single change point in the multivariate beta process. The limiting null distribution and the asymptotic
behavior of the power of the single change point test are derived. The convergence rate of the single change point estimate is also established. For the scenario of multiple change points, we have developed a new wild binary segmentation algorithm to detect the multiple change points. A novel data-driven approach is also designed to consistently estimate the number of change points. Monte Carlo simulation and empirical study show the accuracy and efficiency of our proposed methodologies.
报告人简介：
冯龙现任南开大学统计与数据科学学院副教授、特聘研究员、博士生导师。2022年入选南开大学百名青年学科带头人。主要从事高维高频数据分析方面的研究，在统计学国际顶尖杂志Journal of American Statistical Association、Biometrika、Annals of Statistics、Journal of Econometrics、Journal of Business and Economic Statistics、Technometrics等发表论文30余篇。主持国家自然科学基金面上项目一项，青年基金一项。