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Multiple Change Point Detection in the Mean and Covariance of High-Dimensional Matrix Time Series
This paper studies simultaneous change point detection in the first- and second-order structures of high-dimensional matrix-valued time series. Specifically, we extend the framework of main effect factor models to accommodate multiple change points so that shifts in the mean and covariance structure
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