Simultaneous estimation of linear conditional quantiles with penalized splines
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 1-21 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 141 |
Publication status | Published - 1 Oct 2015 |
Externally published | Yes |
Link(s)
Abstract
We consider smooth estimation of the conditional quantile process in linear models using penalized splines. For linear quantile regression problems, usually separate models are fitted at a finite number of quantile levels and then information from different quantiles is combined in interpreting the results. We propose a smoothing method based on penalized splines that computes the conditional quantiles all at the same time. We consider both fixed-knots and increasing-knots asymptotics of the estimator and show that it converges to a multivariate Gaussian process. Simulations show that smoothing can result in more accurate estimation of the conditional quantiles. The method is further illustrated on a real data set. Empirically (although not theoretically) we observe that the crossing quantile curves problem can often disappear using the smoothed estimator.
Research Area(s)
- Gaussian process, Quantile process, Spline approximation
Citation Format(s)
Simultaneous estimation of linear conditional quantiles with penalized splines. / Lian, Heng; Meng, Jie; Fan, Zengyan.
In: Journal of Multivariate Analysis, Vol. 141, 01.10.2015, p. 1-21.
In: Journal of Multivariate Analysis, Vol. 141, 01.10.2015, p. 1-21.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review