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

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  • Holger Dette
  • Guangming Pan
  • Qing Yang

Abstract

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. Supplementary materials for this article are available online.

Suggested Citation

  • Holger Dette & Guangming Pan & Qing Yang, 2022. "Estimating a Change Point in a Sequence of Very High-Dimensional Covariance Matrices," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 444-454, January.
    • Handle: RePEc:taf:jnlasa:v:117:y:2022:i:537:p:444-454
    DOI: 10.1080/01621459.2020.1785477
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    Cited by:

    1. Liu, Jingyuan & Sun, Ao & Ke, Yuan, 2024. "A generalized knockoff procedure for FDR control in structural change detection," Journal of Econometrics, Elsevier, vol. 239(2).

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