报告人：于怡
报告地点：腾讯会议ID:603 349 652
报告时间：2022年11月07日星期一16:00-17:00
报告摘要:
This talk concerns about the limiting distributions of change point estimators, in a high-dimensional linear regression time series context, where a regression object is observed at every time point 1 ≤t≤ n. At unknown time points, called change points, the regression coefficients change, with the jump sizes measured in -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 their own interest, including a novel variant of the dynamic programming algorithm to boost the computational efficiency, consistent change point localisation rates under functional dependence and a new Bernstein inequality for data possessing functional dependence.
The paper is available at http://arxiv.org/abs/2207.12453
主讲人简介：
报告人于怡，现任英国华威大学的准教授（Reader）并且是艾伦图灵研究所研究员。2013年博士毕业于复旦大学数学统计学院，师从应志良教授；后在英国剑桥大学做博士后，合作导师是Richard Samworth 教授。
