.png){kind=icon}
返回
Sparse quantile regression
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2023.02.014.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We consider both l0-penalized and l0-constrained quantile regression estimators. For the l0-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation errors. We also derive analogous results for the l0-constrained estimator. The resulting rates of convergence are nearly minimax-optimal and the same as those for l1-penalized and non-convex penalized estimators. Further, we characterize expected Hamming loss for the l0- penalized estimator. We implement the proposed procedure via mixed integer linear programming and also a more scalable first-order approximation algorithm. We illustrate the finite-sample performance of our approach in Monte Carlo experiments and its usefulness in a real data application concerning conformal prediction of infant birth weights (with n & AP; 103 and up to p > 103). In sum, our l0-based method produces a much sparser estimator than the l1-penalized and non-convex penalized approaches without compromising precision. & COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
Quantile regression
Sparse estimation
Mixed integer optimization
Finite sample property
Conformal prediction
Hamming distance
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
5.2K
被引数:
3.0W
机构
引用论文
GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA基于超高维数据的全局自适应分位数回归.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏分享收藏
查看更多内容