A1464
Title: Segmenting the time series via self-normalization
Authors: Feiyu Jiang - Fudan University (China) [presenting]
Xiaofeng Shao - University of Illinois at Urbana-Champaign (United States)
Zifeng Zhao - University of Notre Dame (United States)
Abstract: A novel and unified framework is proposed for change-point estimation in multivariate time series. The method is fully nonparametric, enjoys effortless tuning and is robust to temporal dependence. Moreover, it treats change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the core of our method, we couple the self-normalization (SN) based tests with a novel nested local-window segmentation algorithm, which seems new in the growing literature of change-point analysis. Due to the presence of an inconsistent long-run variance estimator in the SN test, non-standard theoretical arguments are further developed to derive the consistency and convergence rate of the proposed SN-based change-point detection method. Extensive numerical experiments and relevant real data analysis are conducted to illustrate the effectiveness and broad applicability of the method in comparison with state-of-the-art approaches in the literature.
