Nonparametric estimation of an additive quantile regression model

• 成果类型: Article
• 署名作者: Horowitz, JL; Lee, S
• 署名单位: Northwestern University; University of London; London School Economics & Political Science; University College London
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000000583
• 发表日期: 2005
• 页码: 1238-1249
• 关键词: semiparametric estimation; local asymptotics; index models; splines; CONVERGENCE; parameters

摘要

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.