A Composite Likelihood-Based Approach for Change-Point Detection in Spatio-Temporal Processes

• 成果类型: Article
• 署名作者: Zhao, Zifeng; Ma, Ting Fung; Ng, Wai Leong; Yau, Chun Yip
• 署名单位: University of Notre Dame; University of South Carolina System; University of South Carolina Columbia; Hang Seng University of Hong Kong; Chinese University of Hong Kong
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2302200
• 发表日期: 2024
• 页码: 3086-3100
• 关键词: time-series; break detection; MODEL; SPACE; segmentation; inference

摘要

This article develops a unified and computationally efficient method for change-point estimation along the time dimension in a nonstationary spatio-temporal process. By modeling a nonstationary spatio-temporal process as a piecewise stationary spatio-temporal process, we consider simultaneous estimation of the number and locations of change-points, and model parameters in each segment. A composite likelihood-based criterion is developed for change-point and parameter estimation. Under the framework of increasing domain asymptotics, theoretical results including consistency and distribution of the estimators are derived under mild conditions. In contrast to classical results in fixed dimensional time series that the localization error of change-point estimator is O-p(1), exact recovery of true change-points is possible in the spatio-temporal setting. More surprisingly, the consistency of change-point estimation can be achieved without any penalty term in the criterion function. In addition, we further establish consistency of the change-point estimator under the infill asymptotics framework where the time domain is increasing while the spatial sampling domain is fixed. A computationally efficient pruned dynamic programming algorithm is developed for the challenging criterion optimization problem. Extensive simulation studies and an application to the U.S. precipitation data are provided to demonstrate the effectiveness and practicality of the proposed method. Supplementary materials for this article are available online.