Fast nonparametric quantile regression with arbitrary smoothing methods

AMS Citation:
Oh, H. -S., T. C. M. Lee, and D. Nychka, 2011: Fast nonparametric quantile regression with arbitrary smoothing methods. Journal of Computational and Graphical Statistics,.
Date:2011-06-01
Resource Type:article
Title:Fast nonparametric quantile regression with arbitrary smoothing methods
Abstract: The calculation of nonparametric quantile regression curve estimates is often computational intensive, as typically an expensive nonlinear optimization problem is involved. This paper proposes a fast and easy-to-implement method for computing such estimates. The main idea is to approximate the costly nonlinear optimization by a sequence of well-studied penalized least-squares type nonparametric mean regression estimation problems. The new method can be paired with different nonparametric smoothing methods and can also be applied to higher dimensional settings. Therefore, it provides a unified framework for computing different types of nonparametric quantile regression estimates, and it also greatly broadens the scope of the applicability of quantile regression methodology. This wide-applicability and the practical performance of the proposed method are illustrated with smoothing spline and wavelet curve estimators, for both uni- and bivariate settings. Results from numerical experiments suggest that estimates obtained from the proposed method are superior to many competitors.
Subject(s):Bivariate quantile regression, Nonparametric regression, Pseudo data, Regression quantile, Wavelets
Peer Review:Refereed
Copyright Information:This is an electronic version of an article published in Oh, H.-S., Lee, T.C., Nychka, D., 2011: Fast nonparametric quantile regression with arbitrary smoothing methods. Journal of Computational and Graphical Statistics, 20 (2), 510-526. Journal of the American Statistical Association is available online at: http://www.tandfonline.com/openurl?genre=article&issn=1061-8600&volume=20&issue=2&spage=510
OpenSky citable URL: ark:/85065/d7057hhf
Author(s):
  • Hee-Seok Oh
  • Thomas Lee
  • Doug Nychka - NCAR/UCAR
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