B. Kai, R. Li and H. Zou, “Local Composite Quantile Regression Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression,” Journal of the Royal Statistical Society, Series B, Vol. 72, No. 1, 2010, pp. 49 69. doi:10.1111/j.1467-9868.2009.00725.x

has been cited by the following article:

• TITLE: Composite Quantile Regression for Nonparametric Model with Random Censored Data

  AUTHORS: Rong Jiang, Weimin Qian

  KEYWORDS: Kaplan-Meier Estimator; Censored Data; Composite Quantile Regression; Kernel Estimator; Nonparametric Model

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.2, April 18, 2013

  ABSTRACT: The composite quantile regression should provide estimation efficiency gain over a single quantile regression. In this paper, we extend composite quantile regression to nonparametric model with random censored data. The asymptotic normality of the proposed estimator is established. The proposed methods are applied to the lung cancer data. Extensive simulations are reported, showing that the proposed method works well in practical settings.