Electronic Journal of Statistics

Change-point detection in high-dimensional covariance structure

Valeriy Avanesov and Nazar Buzun

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In this paper we introduce a novel approach for an important problem of break detection. Specifically, we are interested in detection of an abrupt change in the covariance structure of a high-dimensional random process – a problem, which has applications in many areas e.g., neuroimaging and finance. The developed approach is essentially a testing procedure involving a choice of a critical level. To that end a non-standard bootstrap scheme is proposed and theoretically justified under mild assumptions. Theoretical study features a result providing guaranties for break detection. All the theoretical results are established in a high-dimensional setting (dimensionality $p\gg n$). Multiscale nature of the approach allows for a trade-off between sensitivity of break detection and localization. The approach can be naturally employed in an on-line setting. Simulation study demonstrates that the approach matches the nominal level of false alarm probability and exhibits high power, outperforming a recent approach.

Article information

Electron. J. Statist., Volume 12, Number 2 (2018), 3254-3294.

Received: May 2017
First available in Project Euclid: 5 October 2018

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Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62H15: Hypothesis testing
Secondary: 91B84: Economic time series analysis [See also 62M10] 62P10: Applications to biology and medical sciences

Multiscale bootstrap structural change critical value precision matrix

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Avanesov, Valeriy; Buzun, Nazar. Change-point detection in high-dimensional covariance structure. Electron. J. Statist. 12 (2018), no. 2, 3254--3294. doi:10.1214/18-EJS1484. https://projecteuclid.org/euclid.ejs/1538705038

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