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Heagerty, P. and Pepe, M. (1999) Semiparametric Estimation of Regression Quantiles with Application to Standardizing Weight for Height and Age in U.S. Children. Journal of the Royal Statistical Society, Series C, 48, 533-551.
https://doi.org/10.1111/1467-9876.00170

has been cited by the following article:

• TITLE: Quantile Regression Based on Laplacian Manifold Regularizer with the Data Sparsity in l1 Spaces

  AUTHORS: Ru Feng, Shuang Chen, Lanlan Rong

  KEYWORDS: Semi-Supervised Learning, Conditional Quantile Regression, l1-Regularizer, Manifold-Regularizer, Pinball Loss

  JOURNAL NAME: Open Journal of Statistics, Vol.7 No.5, October 23, 2017

  ABSTRACT: In this paper, we consider the regularized learning schemes based on l1-regularizer and pinball loss in a data dependent hypothesis space. The target is the error analysis for the quantile regression learning. There is no regularized condition with the kernel function, excepting continuity and boundness. The graph-based semi-supervised algorithm leads to an extra error term called manifold error. Part of new error bounds and convergence rates are exactly derived with the techniques consisting of l1-empirical covering number and boundness decomposition.