A pairwise likelihood-based approach for changepoint detection in multivariate time series models

• 成果类型: Article
• 署名作者: Ma, Ting Fung; Yau, Chun Yip
• 署名单位: Chinese University of Hong Kong
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asw002
• 发表日期: 2016
• 页码: 409421
• 关键词: minimum description length; change-point analysis; composite likelihood; break detection; inference; selection; segmentation; SEQUENCES; PRINCIPLE

摘要

This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples demonstrate the statistical and computational efficiency of the proposed method.