Develops methodology and asymptotic theory for single and multiple change point recovery in high-dimensional two-directional mean processes, with climate data application.
High-dimensional change-point detection with sparse alternatives
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abstract
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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stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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High Dimensional Change Point Models for Two-Directional Data
Develops methodology and asymptotic theory for single and multiple change point recovery in high-dimensional two-directional mean processes, with climate data application.