Translator Disclaimer
February 2007 Quantile regression with varying coefficients
Mi-Ok Kim
Ann. Statist. 35(1): 92-108 (February 2007). DOI: 10.1214/009053606000000966

Abstract

Quantile regression provides a framework for modeling statistical quantities of interest other than the conditional mean. The regression methodology is well developed for linear models, but less so for nonparametric models. We consider conditional quantiles with varying coefficients and propose a methodology for their estimation and assessment using polynomial splines. The proposed estimators are easy to compute via standard quantile regression algorithms and a stepwise knot selection algorithm. The proposed Rao-score-type test that assesses the model against a linear model is also easy to implement. We provide asymptotic results on the convergence of the estimators and the null distribution of the test statistic. Empirical results are also provided, including an application of the methodology to forced expiratory volume (FEV) data.

Citation

Download Citation

Mi-Ok Kim. "Quantile regression with varying coefficients." Ann. Statist. 35 (1) 92 - 108, February 2007. https://doi.org/10.1214/009053606000000966

Information

Published: February 2007
First available in Project Euclid: 6 June 2007

zbMATH: 1114.62051
MathSciNet: MR2332270
Digital Object Identifier: 10.1214/009053606000000966

Subjects:
Primary: 62G08, 62G35

Rights: Copyright © 2007 Institute of Mathematical Statistics

JOURNAL ARTICLE
17 PAGES


SHARE
Vol.35 • No. 1 • February 2007
Back to Top