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Nonparametric change-point detection with sparse alternatives

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Inference for Change-Point and Related Processes

We consider the problem of detecting the change in mean in a sequence of Gaussian vectors. We assume that the change happens only in some of the components of the vector. We construct a nonparametric testing procedure that is adaptive to the number of changing components. Under high-dimensional assumptions we obtain the detection boundary and show the rate optimality of the test.

This talk is part of the Isaac Newton Institute Seminar Series series.

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