The Annals of Statistics

High-dimensional change-point detection under sparse alternatives

Farida Enikeeva and Zaid Harchaoui

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We consider the problem of detecting a change in mean in a sequence of high-dimensional Gaussian vectors. The change in mean may be occurring simultaneously in an unknown subset components. We propose a hypothesis test to detect the presence of a change-point and establish the detection boundary in different regimes under the assumption that the dimension tends to infinity and the length of the sequence grows with the dimension. A remarkable feature of the proposed test is that it does not require any knowledge of the subset of components in which the change in mean is occurring and yet automatically adapts to yield optimal rates of convergence over a wide range of statistical regimes.

Article information

Ann. Statist., Volume 47, Number 4 (2019), 2051-2079.

Received: February 2014
Revised: March 2018
First available in Project Euclid: 21 May 2019

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G10: Hypothesis testing 62H15: Hypothesis testing
Secondary: 60G35: Signal detection and filtering [See also 62M20, 93E10, 93E11, 94Axx]

Change-point problem high-dimensional data minimax optimality sparsity


Enikeeva, Farida; Harchaoui, Zaid. High-dimensional change-point detection under sparse alternatives. Ann. Statist. 47 (2019), no. 4, 2051--2079. doi:10.1214/18-AOS1740.

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Supplemental materials

  • Supplement to “High-dimensional change-point detection under sparse alternatives”. The supplementary material [13] contains omitted proofs and some additional simulation results.