# 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} }

`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

Communication-Efficient Distributed Quantile Regression with Optimal Statistical Guarantees

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- 2021

We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions. This is challenging due to the nonsmooth nature of the quantile…

yaglm: a Python package for fitting and tuning generalized linear models that supports structured, adaptive and non-convex penalties

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- 2021

The yaglm package aims to make the broader ecosystem of modern generalized linear models accessible to data analysts and researchers. This ecosystem encompasses a range of loss functions (e.g.…

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