Variable selection for non-parametric quantile regression via smoothing spline analysis of variance

Chen Yen Lin, Howard Bondell, Hao Zhang, Hui Zou

Research output: Contribution to journalArticle

10 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)255-268
Number of pages14
Issue number1
Publication statusPublished - 2013



  • Kernel quantile regression
  • Model selection
  • Reproducing kernel Hilbert space

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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