Nonconvex penalized reduced rank regression and its oracle properties in high dimensions
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Publication:900821
DOI10.1016/j.jmva.2015.09.023zbMath1328.62428OpenAlexW1763893606MaRDI QIDQ900821
Publication date: 23 December 2015
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2015.09.023
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Cites Work
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- Reduced rank regression via adaptive nuclear norm penalization
- Nearly unbiased variable selection under minimax concave penalty
- Optimal selection of reduced rank estimators of high-dimensional matrices
- Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
- Reduced-rank regression for the multivariate linear model
- Asymptotic distribution of the reduced rank regression estimator under general conditions
- Statistics on special manifolds
- Least squares after model selection in high-dimensional sparse models
- Simultaneous analysis of Lasso and Dantzig selector
- Global optimality of nonconvex penalized estimators
- Extended Bayesian information criteria for model selection with large model spaces
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
- Nonconcave Penalized Likelihood With NP-Dimensionality
- Smoothly Clipped Absolute Deviation on High Dimensions
- Reduced Rank Stochastic Regression with a Sparse Singular value Decomposition
- High Dimensional Thresholded Regression and Shrinkage Effect
- Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions
- Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
- A general theory of concave regularization for high-dimensional sparse estimation problems
