Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes
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Publication:97737
DOI10.48550/arXiv.2006.03572arXiv2006.03572OpenAlexW3033324301MaRDI QIDQ97737
Rebecca Willett, Daren Wang, Yi Yu, Rebecca Willett, Yi Yu, Daren Wang
Publication date: 5 June 2020
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2006.03572
high-dimensional statisticspiecewise stationaritypenalized dynamic programmingself-exciting Poisson process
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Cites Work
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- On optimal multiple changepoint algorithms for large data
- Change-point detection in panel data via double CUSUM statistic
- Break detection in the covariance structure of multivariate time series models
- Relevant change points in high dimensional time series
- Univariate mean change point detection: penalization, CUSUM and optimality
- Reactive point processes: a new approach to predicting power failures in underground electrical systems
- Extended BIC for small-n-large-P sparse GLM
- Tracking Dynamic Point Processes on Networks
- The Stationary Bootstrap
- High Dimensional Change Point Estimation via Sparse Projection
- Optimal Detection of Changepoints With a Linear Computational Cost
- A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
- Learning High-Dimensional Generalized Linear Autoregressive Models
- Network Estimation From Point Process Data
- Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
- Spectra of some self-exciting and mutually exciting point processes
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