A Finite Smoothing Algorithm for Quantile Regression

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Quantile regression neural networks: Implementation in R and application to precipitation downscaling (2011, methodology): “A simpliﬁed form of the ﬁnite smoothing algorithm used by Chen (2007) is applied here.”
A fast and efficient implementation of qualitatively constrained quantile smoothing splines (2007, background): “Recently, Chen and Wei (2005), and Chen (2007) introduced a finite smoothing algorithm which shows promises for problems with a high (greater than 0.”
Bayesian Quantile Regression for Ordinal Models (2016, methodology): “Figure 1 that the check function is not differentiable at the origin and consequently, classical methods rely upon computational techniques such as the simplex algorithm, the interior point algorithm or the smoothing algorithm (Madsen and Nielsen 1993; Chen 2007).”