# Localizing Changes in High-Dimensional Vector Autoregressive Processes

@article{Wang2019LocalizingCI, title={Localizing Changes in High-Dimensional Vector Autoregressive Processes}, author={Daren Wang and Yi Yu and Alessandro Rinaldo and Rebecca M. Willett}, journal={arXiv: Statistics Theory}, year={2019} }

Autoregressive models capture stochastic processes in which past realizations determine the generative distribution of new data; they arise naturally in a variety of industrial, biomedical, and financial settings. Often, a key challenge when working with such data is to determine when the underlying generative model has changed, as this can offer insights into distinct operating regimes of the underlying system. This paper describes a novel dynamic programming approach to localizing changes in…

## 17 Citations

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A review on minimax rates in change point detection and localisation.

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- Computer ScienceJournal of the American Statistical Association
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This work first addresses the problem of detecting a single change point using an exhaustive search algorithm and establishes a finite sample error bound for its accuracy, and extends the results to the case of multiple change points that can grow as a function of the sample size.

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