Sparse and Low-Rank Matrix Quantile Estimation With Application to Quadratic Regression

Cites Work

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A lasso for hierarchical interactions
Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
Von Neumann entropy penalization and low-rank matrix estimation
Statistics for high-dimensional data. Methods, theory and applications.
Estimation of high-dimensional low-rank matrices
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
Optimal selection of reduced rank estimators of high-dimensional matrices
Incremental proximal methods for large scale convex optimization
Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
Globally adaptive quantile regression with ultra-high dimensional data
Convex multi-task feature learning
The composite absolute penalties family for grouped and hierarchical variable selection
Conditional quantile processes based on series or many regressors
Innovated interaction screening for high-dimensional nonlinear classification
Quantile processes for semi and nonparametric regression
\(\ell_1\)-penalized quantile regression in high-dimensional sparse models
High-dimensional graphs and variable selection with the Lasso
Multiplier bootstrap for quantile regression: non-asymptotic theory under random design
Regression Quantiles
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Model Selection for High-Dimensional Quadratic Regression via Regularization
Interaction Screening for Ultrahigh-Dimensional Data
Adaptive Estimation in Two-way Sparse Reduced-rank Regression
Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression
Regularized Matrix Regression
Variable Selection With the Strong Heredity Constraint and Its Oracle Property
Regularization and Variable Selection Via the Elastic Net
Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
Reduced Rank Stochastic Regression with a Sparse Singular value Decomposition
A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers

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