Inference for multiple change points in time series via likelihood ratio scan statistics

• 成果类型: Article
• 署名作者: Yau, Chun Yip; Zhao, Zifeng
• 署名单位: Chinese University of Hong Kong; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12139
• 发表日期: 2016
• 页码: 895-916
• 关键词: AUTOREGRESSIVE PROCESSES; models; algorithm; squares

摘要

We propose a likelihood ratio scan method for estimating multiple change points in piecewise stationary processes. Using scan statistics reduces the computationally infeasible global multiple-change-point estimation problem to a number of single-change-point detection problems in various local windows. The computation can be efficiently performed with order O{nptlog(n)}. Consistency for the estimated numbers and locations of the change points are established. Moreover, a procedure is developed for constructing confidence intervals for each of the change points. Simulation experiments and real data analysis are conducted to illustrate the efficiency of the likelihood ratio scan method.