Sparse Convoluted Rank Regression in High Dimensions

Cites Work

Title not available (Why is that?)
Nearly unbiased variable selection under minimax concave penalty
High-dimensional generalizations of asymmetric least squares regression and their applications
Statistics for high-dimensional data. Methods, theory and applications.
One-step sparse estimates in nonconcave penalized likelihood models
The sparsity and bias of the LASSO selection in high-dimensional linear regression
Slope meets Lasso: improved oracle bounds and optimality
\(\ell_1\)-penalized quantile regression in high-dimensional sparse models
Calibrating nonconvex penalized regression in ultra-high dimension
The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
Strong oracle optimality of folded concave penalized estimation
The fused Kolmogorov filter: a nonparametric model-free screening method
Smoothed quantile regression with large-scale inference
Square-root lasso: pivotal recovery of sparse signals via conic programming
On Cross-Validation for Sparse Reduced Rank Regression
Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
Estimating Regression Coefficients by Minimizing the Dispersion of the Residuals
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
Statistical Foundations of Data Science
Sparse Composite Quantile Regression in Ultrahigh Dimensions With Tuning Parameter Calibration
A Tuning-free Robust and Efficient Approach to High-dimensional Regression
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization
Convergence of a block coordinate descent method for nondifferentiable minimization
Smoothing Quantile Regressions
ADMM for High-Dimensional Sparse Penalized Quantile Regression