|Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA.
|Year of Publication
|Lin, Chen-Yen, Howard Bondell, Hao Helen Zhang, and Hui Zou
Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) 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. Supplementary materials for this article are available online.
|Variable selection for nonparametric quantile regression via smoothing spline ANOVA.
|PubMed Central ID
|P01 CA142538 / CA / NCI NIH HHS / United States
Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA.