centre for microdata methods and practice

ESRC centre

cemmap is an ESRC research centre

ESRC

Keep in touch

Subscribe to cemmap news

Nonparametric estimation of an additive quantile regression model

Authors: Joel L. Horowitz and Sokbae Lee
Date: 16 December 2005
Type: Journal Article, Journal of the American Statistical Association, Vol. 100, No. 472, pp.1238-1249

Abstract

This article is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n-r/(2r+1) when the additive components are r-times continuously differentiable for some r ≥ 2. This result holds regardless of the dimension of the covariates, and thus the new estimator has no curse of dimensionality. In addition, the estimator has an oracle property and is easily extended to a generalized additive quantile regression model with a link function. The numerical performance and usefulness of the estimator are illustrated by Monte Carlo experiments and an empirical example. Download full version
Previous version:
Joel L. Horowitz and Sokbae Lee April 2004, Nonparametric estimation of an additive quantile regression model, cemmap Working Paper, CWP07/04

Publications feeds

Subscribe to cemmap working papers via RSS

Search cemmap

Search by title, topic or name.

Contact cemmap

Centre for Microdata Methods and Practice

How to find us

Tel: +44 (0)20 7291 4800

E-mail us