Quantile regression without the curse of unsmoothness
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:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Keywords:||quantile regression, standard error|
|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|
Repository Staff Only: item control page