Adaptive quantile regression with precise risk bounds
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) | 875-896 |
Journal / Publication | Science China Mathematics |
Volume | 60 |
Issue number | 5 |
Publication status | Published - 1 May 2017 |
Externally published | Yes |
Link(s)
Abstract
An adaptive local smoothing method for nonparametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in the sense that no prior information about the structure of the model is assumed. The fully adaptive feature not only allows varying bandwidths to accommodate jumps or instantaneous slope changes, but also allows the algorithm to be spatially adaptive. Under general conditions, precise risk bounds for homogeneous and heterogeneous cases of the underlying conditional quantile curves are established. An automatic selection algorithm for locally adaptive bandwidths is also given, which is applicable to higher dimensional cases. Simulation studies and data analysis confirm that the proposed methodology works well.
Research Area(s)
- adaptive smoothing, automatic bandwidths, conditional quantile, risk bounds, robustness
Bibliographic Note
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Citation Format(s)
Adaptive quantile regression with precise risk bounds. / Tian, MaoZai; Chan, Ngai Hang.
In: Science China Mathematics, Vol. 60, No. 5, 01.05.2017, p. 875-896.
In: Science China Mathematics, Vol. 60, No. 5, 01.05.2017, p. 875-896.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review