edu.ucar.opensky::articles:18120 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-26T13:34:36.618945 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7057hhf Fast nonparametric quantile regression with arbitrary smoothing methods 2011-06-01T00:00:00Z publication https://n2t.org/ark:/85065/d7057hhf Oh, H. author Lee, T. author Nychka, Douglas author UCAR/NCAR - Library publisher 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication 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 None eng geoscientificInformation