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High-dimensional change-point detection with sparse alternatives

Farida Enikeeva 1 Zaid Harchaoui 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We consider the problem of detection of a simultaneous change in mean in a sequence of Gaussian vectors. We assume that the change occurs only in some of the components of the vector. We construct a procedure of testing the change in mean adaptive to the number of non-zero 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 the test and show its rate-optimality.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-00933185
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Submitted on : Monday, January 20, 2014 - 10:21:02 AM
Last modification on : Thursday, January 20, 2022 - 5:30:15 PM

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  • HAL Id : hal-00933185, version 1
  • ARXIV : 1312.1900

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Farida Enikeeva, Zaid Harchaoui. High-dimensional change-point detection with sparse alternatives. 2013. ⟨hal-00933185⟩

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