报告承办单位: 数学与统计学院

报告题目: Inference for change-points in time series under various break sizes

报告人姓名: 邱俊业

报告人所在单位: 香港中文大学统计系

报告人职称: 教授，博士生导师

报告时间:2026年4月27日, 星期一，16:10-17:40

报告地点:长沙理工大学云塘校区理科楼A-212

邀请人：朱恩文

报告摘要：In this paper, we investigate the asymptotic distribution of a change-point estimator for piecewise stationary time series across different magnitudes of break sizes. Specifically, we examine break sizes of order O(1/nα) for 0 <α < 1/2, α = 1/2, and α > 1/2, corresponding to large, moderate, and small break sizes, respectively, where n denotes the sample size. Our results reveal that the asymptotic distributions in these three regimes differ but are all linked to the maximizer of certain functions of a two-sided drifted Brownian motion. To address the practical challenge of unknown break sizes, we introduce an asymptotically pivotal statistic that is robust across the whole range of break size regimes on α ∈ [0,∞). This statistic provides a unified approach for constructing confidence intervals for the change-point without requiring prior knowledge of the break size. Simulation studies show that the asymptotic inference performs well under different break sizes, while the pivotal statistic demonstrates better performance in most scenarios. Applications to financial time series further highlight the practical relevance of the proposed inference methods.

报告人简介： 香港中文大学统计系教授，2010年博士毕业于哥伦比亚大学，主要的研究方向包括：时间序列，空间统计，环境统计和变点分析等，已在统计的四大JASA、JRSSB、AOS、Biometrika和计量的顶级杂志JOE等杂志发表论文近60篇，同时也是Journal of Time Series Analysis的Associate Editor和International Journal of Mathematics and Statistics的Chief Editor。