Multiple change-points detection in high dimension
DOI10.1142/S201032631950014XzbMath1437.62202OpenAlexW2906442928MaRDI QIDQ5108292
Guosheng Yin, Changliang Zou, Yunlong Wang, Zhaojun Wang
Publication date: 30 April 2020
Published in: Random Matrices: Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s201032631950014x
dynamic programmingasymptotic normalitysparse signalslarge \(p\)small \(n\)feature screeninghigh-dimensional homogeneity test
Nonparametric hypothesis testing (62G10) Estimation in multivariate analysis (62H12) Hypothesis testing in multivariate analysis (62H15) Dynamic programming (90C39)
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- Using penalized contrasts for the change-point problem
- Efficient scalable schemes for monitoring a large number of data streams
- Sequential multi-sensor change-point detection
- Wild binary segmentation for multiple change-point detection
- Common breaks in means and variances for panel data
- The screening and ranking algorithm to detect DNA copy number variations
- Hanson-Wright inequality and sub-Gaussian concentration
- Uniform change point tests in high dimension
- Consistencies and rates of convergence of jump-penalized least squares estimators
- Estimating the number of change-points via Schwarz' criterion
- Multiple change-point detection: a selective overview
- Evaluating stationarity via change-point alternatives with applications to fMRI data
- High-dimensional change-point detection under sparse alternatives
- A two-sample test for high-dimensional data with applications to gene-set testing
- Nonparametric maximum likelihood approach to multiple change-point problems
- Structural breaks in time series
- Multiple change-point detection via a screening and ranking algorithm
- Inference for single and multiple change-points in time series
- Test of Significance Based on Wavelet Thresholding and Neyman's Truncation
- Two-sample behrens-fisher problem for high-dimensional data
- Likelihood Ratio Tests for a Change in the Multivariate Normal Mean
- Estimating and Testing Linear Models with Multiple Structural Changes
- A New Reduced-Rank Linear Discriminant Analysis Method and Its Applications
- A study of two high-dimensional likelihood ratio tests under alternative hypotheses
- Power Enhancement in High-Dimensional Cross-Sectional Tests
- Detecting Changes in the Mean of Functional Observations
- Optimal Detection of Changepoints With a Linear Computational Cost
- A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
- Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
- Change‐point detection in panel data
- DETECTION OF WEAK SIGNALS IN HIGH-DIMENSIONAL COMPLEX-VALUED DATA
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