# High‐dimensional quantile regression: Convolution smoothing and concave regularization

@article{Tan2021HighdimensionalQR,
title={High‐dimensional quantile regression: Convolution smoothing and concave regularization},
author={Kean Ming Tan and Lan Wang and Wen‐Xin Zhou},
journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
year={2021}
}
• Kean Ming Tan, Wen‐Xin Zhou
• Published 12 September 2021
• Mathematics
• Journal of the Royal Statistical Society: Series B (Statistical Methodology)
1-penalized quantile regression is widely used for analyzing high-dimensional data with heterogeneity. It is now recognized that the 1-penalty introduces non-negligible estimation bias, while a proper use of concave regularization may lead to estimators with refined convergence rates and oracle properties as the signal strengthens. Although folded concave penalized M-estimation with strongly convex loss functions have been well studied, the extant literature on quantile regression is…
2 Citations

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