Quantile regression provides a more thorough view of the effect of covariates on a response. Non‐parametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, as important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline analysis of variance models. The proposed sparse non‐parametric quantile regression can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Copyright © 2013 John Wiley & Sons Ltd

中文翻译：

---

通过平滑样条 AN OVA 进行非参数分位数回归的变量选择。


分位数回归可以更全面地了解协变量对响应的影响。非参数分位数回归已成为避免限制性参数假设的可行替代方案。分位数回归的变量选择问题具有挑战性，因为重要变量可以以不同的方式影响各个分位数。我们通过在方差模型的平滑样条分析的背景下通过正则化来解决这个问题。所提出的稀疏非参数分位数回归可以识别重要变量并为分位数提供灵活的估计。我们的数值研究表明新程序在变量选择和函数估计方面具有良好的性能。版权所有 © 2013 约翰·威利父子有限公司