Estimating a Change Point in a Sequence of Very High-Dimensional Covariance Matrices

• 成果类型: Article
• 署名作者: Dette, Holger; Pan, Guangming; Yang, Qing
• 署名单位: Ruhr University Bochum; Nanyang Technological University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1785477
• 发表日期: 2022
• 页码: 444-454
• 关键词: multivariate time-series

摘要

This article considers the problem of estimating a change point in the covariance matrix in a sequence of high-dimensional vectors, where the dimension is substantially larger than the sample size. A two-stage approach is proposed to efficiently estimate the location of the change point. The first step consists of a reduction of the dimension to identify elements of the covariance matrices corresponding to significant changes. In a second step, we use the components after dimension reduction to determine the position of the change point. Theoretical properties are developed for both steps, and numerical studies are conducted to support the new methodology.for this article are available online.