Estimation of general semi-parametric quantile regression

Yan Fan, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)


Quantile regression introduced by Koenker and Bassett (1978) produces a comprehensive picture of a response variable on predictors. In this paper, we propose a general semi-parametric model of which part of predictors are presented with a single-index, to model the relationship of conditional quantiles of the response on predictors. Special cases are single-index models, partially linear single-index models and varying coefficient single-index models. We propose the qOPG, a quantile regression version of outer-product gradient estimation method (OPG, Xia et al., 2002) to estimate the single-index. Large-sample properties, simulation results and a real-data analysis are provided to examine the performance of the qOPG.

Original languageEnglish
Pages (from-to)896-910
Number of pages15
JournalJournal of Statistical Planning and Inference
Issue number5
Publication statusPublished - May 2013

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

User-Defined Keywords

  • Eigenvector
  • Outer-product gradient estimation (OPG)
  • QOPG
  • Quantile regression
  • Single-index model


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