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Multiple Change Point Detection in the Mean and Covariance of High-Dimensional Matrix Time Series
Zetai Cen, Haeran Cho
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 framewor…
Tail-robust estimation of factor-adjusted vector autoregressive models for high-dimensional time series
Dylan Dijk, Haeran Cho
We study the problem of modelling high-dimensional, heavy-tailed time series data via a factor-adjusted vector autoregressive (VAR) model, which simultaneously accounts for pervasi…
Moving sum procedure for multiple change point detection in large factor models
Matteo Barigozzi, Haeran Cho, Lorenzo Trapani
This paper proposes a moving sum methodology for detecting multiple change points in high-dimensional time series under a factor model, where changes are attributed to those in loa…
Tail-robust factor modelling of vector and tensor time series in high dimensions
Matteo Barigozzi, Haeran Cho, Hyeyoung Maeng
We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, which produce extreme observations with non-negligible pr…