D4R: doubly robust reduced rank regression in high dimension

Cites Work

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A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
Reduced rank regression via adaptive nuclear norm penalization
Nearly unbiased variable selection under minimax concave penalty
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Robust reduced-rank modeling via rank regression
Statistics for high-dimensional data. Methods, theory and applications.
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
Optimal selection of reduced rank estimators of high-dimensional matrices
High breakdown-point and high efficiency robust estimates for regression
High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery
Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer
Reduced-rank regression for the multivariate linear model
Multivariate reduced-rank regression
Statistical consistency and asymptotic normality for high-dimensional robust \(M\)-estimators
Robust reduced rank regression in a distributed setting
Dependence in elliptical partial correlation graphs
Adaptive estimation of the rank of the coefficient matrix in high-dimensional multivariate response regression models
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
High-Dimensional Statistics
Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
Reduced rank ridge regression and its kernel extensions
Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression
Regularized Matrix Regression
Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
Selective factor extraction in high dimensions
Robust reduced-rank regression
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Reduced Rank Stochastic Regression with a Sparse Singular value Decomposition
Estimation of Low Rank High-Dimensional Multivariate Linear Models for Multi-Response Data
A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
Robust Lasso Regression Using Tukey's Biweight Criterion

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