Local linear additive quantile regression
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 333-346 |
| Journal / Publication | Scandinavian Journal of Statistics |
| Volume | 31 |
| Issue number | 3 |
| Publication status | Published - Sept 2004 |
| Externally published | Yes |
Link(s)
Abstract
We consider non-parametric additive quantile regression estimation by kernel-weighted local linear fitting. The estimator is based on localizing the characterization of quantile regression as the minimizer of the appropriate 'check function'. A backfitting algorithm and a heuristic rule for selecting the smoothing parameter are explored. We also study the estimation of average-derivative quantile regression under the additive model. The techniques are illustrated by a simulated example and a real data set.
Research Area(s)
- Additive models, Average derivative, Backfitting algorithm, Bandwidth selection, Guantile regression, Local linear fitting
Bibliographic Note
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Citation Format(s)
Local linear additive quantile regression. / Yu, Keming; Lu, Zudi.
In: Scandinavian Journal of Statistics, Vol. 31, No. 3, 09.2004, p. 333-346.
In: Scandinavian Journal of Statistics, Vol. 31, No. 3, 09.2004, p. 333-346.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
