Wild binary segmentation for multiple change-point detection
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Publication:482881
DOI10.1214/14-AOS1245zbMath1302.62075arXiv1411.0858WikidataQ105584282 ScholiaQ105584282MaRDI QIDQ482881
Publication date: 6 January 2015
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1411.0858
Bayesian information criterionthresholdingbinary segmentationchange-point detectionrandomised algorithmsmultiple change-points
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Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Detecting multiple change-points in the mean of Gaussian process by model selection
- Detection of multiple changes in a sequence of dependent variables
- Using penalized contrasts for the change-point problem
- Time-threshold maps: using information from wavelet reconstructions with all threshold values simultaneously
- Wild binary segmentation for multiple change-point detection
- A general criterion to determine the number of change-points
- Consistent multiple testing for change points
- Multiscale interpretation of taut string estimation and its connection to unbalanced Haar wavelets
- Model selection by LASSO methods in a change-point model
- Properties and refinements of the fused Lasso
- Application of modified information criterion to multiple change point problems
- Simultaneous change point analysis and variable selection in a regression problem
- Consistencies and rates of convergence of jump-penalized least squares estimators
- Estimating the number of change-points via Schwarz' criterion
- Algorithms for the optimal identification of segment neighborhoods
- Local extremes, runs, strings and multiresolution. (With discussion)
- Multiscale testing of qualitative hypotheses
- Least angle regression. (With discussion)
- Estimating the number of change points in a sequence of independent normal random variables
- Testing for multiple change points
- Least‐squares Estimation of an Unknown Number of Shifts in a Time Series
- Randomized Algorithms for Matrices and Data
- Multiscale and multilevel technique for consistent segmentation of nonstationary time series
- Maxima of discretely sampled random fields, with an application to 'bubbles'
- Minimax Methods for Multihypothesis Sequential Testing and Change-Point Detection Problems
- Unbalanced Haar Technique for Nonparametric Function Estimation
- On Minimax Estimation of a Discontinuous Signal
- The Influence Curve and Its Role in Robust Estimation
- Sparsity and Smoothness Via the Fused Lasso
- Jump and sharp cusp detection by wavelets
- Optimal Detection of Changepoints With a Linear Computational Cost
- Detection with the scan and the average likelihood ratio
- A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
- Multiple Change-Point Estimation With a Total Variation Penalty
- Circular binary segmentation for the analysis of array-based DNA copy number data
- Structural Break Estimation for Nonstationary Time Series Models
- Statistical methods for DNA sequence segmentation
- Gaussian model selection
- Comments on: ``Extensions of some classical methods in change point analysis