Quantile regression without the curse of unsmoothness

Wang, You-Gan, Shao, Quanxi, & Zhu, Min (2009) Quantile regression without the curse of unsmoothness. Computational Statistics and Data Analysis, 53(10), pp. 3696-3705.

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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.

Impact and interest:

15 citations in Scopus
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14 citations in Web of Science®

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ID Code: 54060
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: quantile regression, standard error
DOI: 10.1016/j.csda.2009.03.012
ISSN: 1872-7352
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Crown Copyright 2009 Elsevier B.V.
Deposited On: 09 Oct 2012 23:29
Last Modified: 17 Nov 2015 01:58

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