Modified path algorithm of fused Lasso signal approximator for consistent recovery of change points
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Publication:2317289
DOI10.1016/j.jspi.2018.10.003zbMath1432.62110OpenAlexW2898510200WikidataQ129077585 ScholiaQ129077585MaRDI QIDQ2317289
Publication date: 9 August 2019
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2018.10.003
Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)
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Uses Software
Cites Work
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- Estimating the number of change-points via Schwarz' criterion
- Penalized quasi-likelihood estimation in partial linear models
- On stepwise pattern recovery of the fused Lasso
- Local extremes, runs, strings and multiresolution. (With discussion)
- Adaptive piecewise polynomial estimation via trend filtering
- Pathwise coordinate optimization
- $\ell_1$ Trend Filtering
- Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
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