On stepwise pattern recovery of the fused Lasso

DOI10.1016/j.csda.2015.08.013zbMath1468.62161arXiv1211.5194OpenAlexW2200384000MaRDI QIDQ1660156

Jinzhu Jia, Junyang Qian

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1211.5194

zbMATH Keywords

preconditioningfused Lassonon-asymptoticpattern recovery

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07)

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Cites Work

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• The solution path of the generalized lasso
• Properties and refinements of the fused Lasso
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
• Asymptotics for Lasso-type estimators.
• Preconditioning the Lasso for sign consistency
• Pathwise coordinate optimization
• High-dimensional graphs and variable selection with the Lasso
• A Singular Value Thresholding Algorithm for Matrix Completion
• Stable recovery of sparse overcomplete representations in the presence of noise
• Just relax: convex programming methods for identifying sparse signals in noise
• Spatial smoothing and hot spot detection for CGH data using the fused lasso
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Information-Theoretic Limits on Sparsity Recovery in the High-Dimensional and Noisy Setting
• Multiple Change-Point Estimation With a Total Variation Penalty