A component lasso
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Publication:3463403
DOI10.1002/cjs.11267zbMath1329.62326arXiv1311.4472OpenAlexW2963982265MaRDI QIDQ3463403
Robert Tibshirani, Nadine Hussami
Publication date: 14 January 2016
Published in: Canadian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1311.4472
connected componentssparsityLassographical Lassoelastic netnon-negative least squaresstrong irrepresentable condition
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- Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization
- Correlated variables in regression: clustering and sparse estimation
- Relaxed Lasso
- The cluster graphical Lasso for improved estimation of Gaussian graphical models
- Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
- Asymptotics for Lasso-type estimators.
- The graphical lasso: new insights and alternatives
- On the conditions used to prove oracle results for the Lasso
- Sign-constrained least squares estimation for high-dimensional regression
- Sparsity oracle inequalities for the Lasso
- High-dimensional graphs and variable selection with the Lasso
- Covariance-Regularized Regression and Classification for high Dimensional Problems
- Hybrid hierarchical clustering with applications to microarray data
- Greed is Good: Algorithmic Results for Sparse Approximation
- Just relax: convex programming methods for identifying sparse signals in noise
- Averaged gene expressions for regression
- Atomic Decomposition by Basis Pursuit
- A Model-Averaging Approach for High-Dimensional Regression
- Regularization and Variable Selection Via the Elastic Net
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Discussion of ``Correlated variables in regression: clustering and sparse estimation
