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A sparse dimension-reduced subspace-based approach for detecting multiple change points in high-dimensional data

Author

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  • Yu, Luoyao
  • Zhao, Rongzhu
  • Huang, Jiaqi
  • Zhu, Lixing
  • Zhu, Xuehu

Abstract

This paper develops a novel penalized matrix estimation method for sparse dimension reduction when detecting change points in high-dimensional data. The strategy is to project high-dimensional data onto a low-dimensional subspace without losing any change point information, enabling efficient change point detection within this dimension-reduced subspace. Theoretical analysis establishes the consistency of the proposed matrix estimation and selects consistently the important variables which have change points. Numerical studies on synthetic and several real data sets suggest that the dimension reduction strategy enhances the performance of existing approaches. Additionally, the results showcase the efficiency of the proposed algorithm for selecting important variables in high-dimensional sparse data.

Suggested Citation

  • Yu, Luoyao & Zhao, Rongzhu & Huang, Jiaqi & Zhu, Lixing & Zhu, Xuehu, 2026. "A sparse dimension-reduced subspace-based approach for detecting multiple change points in high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:jmvana:v:213:y:2026:i:c:s0047259x25001897
    DOI: 10.1016/j.jmva.2025.105594
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