Abstract
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 language | English |
|---|---|
| Pages (from-to) | 896-910 |
| Number of pages | 15 |
| Journal | Journal of Statistical Planning and Inference |
| Volume | 143 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2013 |
User-Defined Keywords
- Eigenvector
- Outer-product gradient estimation (OPG)
- QOPG
- Quantile regression
- Single-index model
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