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High-dimensional change-point detection with sparse alternatives

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arxiv 1312.1900 v2 pith:7G3QCU44 submitted 2013-12-06 math.ST stat.TH

Farida Enikeeva , Zaid Harchaoui This is my paper · ORCID
classification math.STstat.TH
keywords changechange-pointcomponentsdetectiondimensionproblemsequenceunder
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We consider the problem of detecting a change in mean in a sequence of Gaussian vectors. Under the alternative hypothesis, the change occurs only in some subset of the components of the vector. We propose a test of the presence of a change-point that is adaptive to the number of changing components. Under the assumption that the vector dimension tends to infinity and the length of the sequence grows slower than the dimension of the signal, we obtain the detection boundary for this problem and prove its rate-optimality.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Subset Multivariate Collective And Point Anomaly Detection

    stat.ME 2019-09 conditional novelty 7.0 of 10

    MVCAPA uses a penalised saving statistic and dynamic programming to consistently detect multiple sparse or dense collective anomalies in multivariate time series, with finite-sample false positive control and support ...

  2. High Dimensional Change Point Models for Two-Directional Data

    stat.ME 2026-06 unverdicted novelty 4.0 of 10

    Develops methodology and asymptotic theory for single and multiple change point recovery in high-dimensional two-directional mean processes, with climate data application.

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