Fused Lasso penalized least absolute deviation estimator for high dimensional linear regression
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Publication:1713210
DOI10.3934/naco.2018006zbMath1461.62021OpenAlexW2789724145MaRDI QIDQ1713210
Xianchao Xiu, Yanqing Liu, Huan Zhang, Jiyuan Tao, Lingchen Kong
Publication date: 24 January 2019
Published in: Numerical Algebra, Control and Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3934/naco.2018006
least absolute deviationhigh dimensional linear regressionfused Lassolinearized alternating direction method of multipliers
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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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization
- The \(L_1\) penalized LAD estimator for high dimensional linear regression
- Statistics for high-dimensional data. Methods, theory and applications.
- A unified primal-dual algorithm framework based on Bregman iteration
- Properties and refinements of the fused Lasso
- Model selection in multivariate adaptive regression splines (MARS) using information complexity as the fitness function
- Stability analysis for stochastic differential equations with infinite Markovian switchings
- Multivariate adaptive regression splines
- Linearized alternating direction method of multipliers for sparse group and fused Lasso models
- A convex version of multivariate adaptive regression splines
- Strong convergence of extragradient method for generalized variational inequalities in Hilbert space
- On the global and linear convergence of the generalized alternating direction method of multipliers
- Pathwise coordinate optimization
- Coordinate descent algorithms for lasso penalized regression
- An augmented Lagrangian based parallel splitting method for separable convex minimization with applications to image processing
- SCAD-Penalized Least Absolute Deviation Regression in High-Dimensional Models
- Decoding by Linear Programming
- Spatial smoothing and hot spot detection for CGH data using the fused lasso
- Shifting Inequality and Recovery of Sparse Signals
- Sparsity and Smoothness Via the Fused Lasso
- The Linearized Alternating Direction Method of Multipliers for Dantzig Selector
- Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
- Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection
- Variable Selection and Model Building via Likelihood Basis Pursuit