Inference of Breakpoints in High-dimensional Time Series
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Publication:6110713
DOI10.1080/01621459.2021.1893178zbMath1515.62040OpenAlexW3152913909MaRDI QIDQ6110713
Likai Chen, Weining Wang, Wei-Biao Wu
Publication date: 6 July 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://eprints.whiterose.ac.uk/171296/1/SSRN_id3378221.pdf
multiple change-point detectionGaussian approximationinference of break locationstemporal and cross-sectional dependence
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Cites Work
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- A MOSUM procedure for the estimation of multiple random change points
- Change-point detection in panel data via double CUSUM statistic
- Wild binary segmentation for multiple change-point detection
- Common breaks in means and variances for panel data
- Uniform change point tests in high dimension
- The location of the maximum and asymmetric two-sided Brownian motion with triangular drift
- Subsampling
- Resampling methods for dependent data
- Gaussian approximation for high dimensional time series
- Challenging the empirical mean and empirical variance: a deviation study
- Tail-greedy bottom-up data decompositions and fast multiple change-point detection
- Minimax rates in sparse, high-dimensional change point detection
- Detection and localization of change-points in high-dimensional network traffic data
- High-dimensional change-point detection under sparse alternatives
- Central limit theorems and bootstrap in high dimensions
- Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
- Detecting simultaneous changepoints in multiple sequences
- Spatial smoothing and hot spot detection for CGH data using the fused lasso
- Estimating and Testing Linear Models with Multiple Structural Changes
- Sample Splitting and Threshold Estimation
- High Dimensional Change Point Estimation via Sparse Projection
- Bayesian Model for Multiple Change-Points Detection in Multivariate Time Series
- A Cluster Analysis Method for Grouping Means in the Analysis of Variance
- Optimal Detection of Changepoints With a Linear Computational Cost
- Inference of Trends in Time Series
- Testing for Trends in High-Dimensional Time Series
- Circular binary segmentation for the analysis of array-based DNA copy number data
- Detection of Multiple Structural Breaks in Multivariate Time Series
- Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- The Lasso for High Dimensional Regression with a Possible Change Point