Nonparametric estimation of an additive quantile regression model

Joel L. Horowitz*, Sokbae Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

98 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1238-1249
Number of pages12
JournalJournal of the American Statistical Association
Volume100
Issue number472
DOIs
StatePublished - Dec 2005

Keywords

  • Additive model
  • Dimension reduction
  • Local polynomial estimation
  • Nonparametric regression
  • Quantile regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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