Quantile regression without the curse of unsmoothness

Wang, You-Gan, Shao, Quanxi and Zhu, Min (2009) Quantile regression without the curse of unsmoothness. Computational Statistics & Data Analysis, 53 10: 3696-3705. doi:10.1016/j.csda.2009.03.012


Author Wang, You-Gan
Shao, Quanxi
Zhu, Min
Title Quantile regression without the curse of unsmoothness
Journal name Computational Statistics & Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2009-08
Sub-type Article (original research)
DOI 10.1016/j.csda.2009.03.012
Volume 53
Issue 10
Start page 3696
End page 3705
Total pages 10
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract We consider quantile regression models and investigate the induced smoothing method for obtaining the covariance matrix of the regression parameter estimates. We show that the difference between the smoothed and unsmoothed estimating functions in quantile regression is negligible. The detailed and simple computational algorithms for calculating the asymptotic covariance are provided. Intensive simulation studies indicate that the proposed method performs very well. We also illustrate the algorithm by analyzing the rainfall-runoff data from Murray Upland, Australia.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
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Created: Wed, 17 Nov 2010, 14:08:43 EST