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.
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- Koenker,Roger, 2005.
Cambridge University Press, number 9780521608275, November.
- Yvonne H. S. Ho & Stephen M. S. Lee, 2005. "Calibrated interpolated confidence intervals for population quantiles," Biometrika, Biometrika Trust, vol. 92(1), pages 234-241, March.
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- B. M. Brown & You-Gan Wang, 2005. "Standard errors and covariance matrices for smoothed rank estimators," Biometrika, Biometrika Trust, vol. 92(1), pages 149-158, March.
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