统计与数据科学系系列学术报告之四百零七期

时    间：2023年8月24日（周四）16:00-17:00

主持人：复旦大学管理学院 统计与数据科学系 蒋斐宇 青年副研究员

地    点：史带楼602室

报告人：Haotian Xu Assistant Professor

              Department of Statistics at University of Warwick

题   目：Change point inference in high-dimensional regression models under temporal dependence

摘   要：In this talk, we focus on the limiting distributions of change point estimators, in a high-dimensional linear regression time series context, where a regression object (y_t, X_t) ∈ R × R^p is observed at every time point t ∈ {1, . . . , n}. At unknown time points, called change points, the regression coefficients change, with the jump sizes measured in l_2-norm. We provide limiting distributions of the change point estimators in the regimes where the minimal jump size vanishes and where it remains a constant. We allow for both the covariate and noise sequences to be temporally dependent, in the functional dependence framework, which is the first time seen in the change point inference literature. We show that a block-type long-run variance estimator is consistent under the functional dependence, which facilitates the practical implementation of our derived limiting distributions. We also present a few important byproducts of our analysis, which are of their own interest. These include a novel variant of the dynamic programming algorithm to boost the computational efficiency, consistent change point localisation rates under temporal dependence and a new Bernstein inequality for data possessing functional dependence. Extensive numerical results are provided to support our theoretical results. The proposed methods are implemented in the R package “changepoints".

个人简介：Haotian Xu is an incoming Harrison Early Career Assistant Professor in the Department of Statistics at University of Warwick starting in September 2023. He previously held postdoctoral positions at Penn State University, Université Catholique de Louvain and University of Warwick. He received his PhD in Statistics from University of Geneva in 2021.

 统计与数据科学系

2023-8-15