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Inference for Change Points in High Dimensional Mean Shift Models
Component-wise distributions are characterized under both vanishing and non-vanishing jump size regimes, while joint distributions for any finite subset of change point estimates are characterized under the latter regime, which also yields asymptotic independence of these estimates.
Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees
Natural extensions of the single $2d$-change point estimation methodology are provided.
Inference on the change point in high dimensional time series models via plug in least squares
We study a plug in least squares estimator for the change point parameter where change is in the mean of a high dimensional random vector under subgaussian or subexponential distributions.
Inference on the Change Point for High Dimensional Dynamic Graphical Models
We develop an estimator for the change point parameter for a dynamically evolving graphical model, and also obtain its asymptotic distribution under high dimensional scaling.
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