Detecting Changes in Slope With an L0 Penalty
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Publication:3391229
DOI10.1080/10618600.2018.1512868OpenAlexW2575850544MaRDI QIDQ3391229
Robert Maidstone, Paul Fearnhead, Adam N. Letchford
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2018.1512868
Related Items (8)
Narrowest-Over-Threshold Detection of Multiple Change Points and Change-Point-Like Features ⋮ Generalized multiple change-point detection in the structure of multivariate, possibly high-dimensional, data sequences ⋮ Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise ⋮ Localising change points in piecewise polynomials of general degrees ⋮ Detecting multiple generalized change-points by isolating single ones ⋮ Smoothing for signals with discontinuities using higher order Mumford-Shah models ⋮ Consistency of a range of penalised cost approaches for detecting multiple changepoints ⋮ Time series analysis of COVID-19 infection curve: a change-point perspective
Uses Software
Cites Work
- On optimal multiple changepoint algorithms for large data
- A pruned dynamic programming algorithm to recover the best segmentations with $1$ to $K_{max}$ change-points
- Adaptively refined dynamic program for linear spline regression
- Wild binary segmentation for multiple change-point detection
- Estimating the number of change-points via Schwarz' criterion
- Estimating the dimension of a model
- Minimax estimation of sharp change points
- Evaluating stationarity via change-point alternatives with applications to fMRI data
- A computationally efficient nonparametric approach for changepoint detection
- Adaptive piecewise polynomial estimation via trend filtering
- Least‐squares Estimation of an Unknown Number of Shifts in a Time Series
- $\ell_1$ Trend Filtering
- The Influence Curve and Its Role in Robust Estimation
- Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments
- High Dimensional Change Point Estimation via Sparse Projection
- A Cluster Analysis Method for Grouping Means in the Analysis of Variance
- Optimal Detection of Changepoints With a Linear Computational Cost
- Narrowest-Over-Threshold Detection of Multiple Change Points and Change-Point-Like Features
- Iterative Potts and Blake–Zisserman minimization for the recovery of functions with discontinuities from indirect measurements
- A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data
- Structural Break Estimation for Nonstationary Time Series Models
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