Liang, H. and Li., R.Z. (2009) Variable Selection for Partially Linear Models with Measurement Errors. Journal of the American Statistical Association, 104, 234-248.
http://dx.doi.org/10.1198/jasa.2009.0127

has been cited by the following article:

• TITLE: Double-Penalized Quantile Regression in Partially Linear Models

  AUTHORS: Yunlu Jiang

  KEYWORDS: Quantile Regression, Partially Linear Model, Heavy-Tailed Distribution

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.2, April 23, 2015

  ABSTRACT: In this paper, we propose the double-penalized quantile regression estimators in partially linear models. An iterative algorithm is proposed for solving the proposed optimization problem. Some numerical examples illustrate that the finite sample performances of proposed method perform better than the least squares based method with regard to the non-causal selection rate (NSR) and the median of model error (MME) when the error distribution is heavy-tail. Finally, we apply the proposed methodology to analyze the ragweed pollen level dataset.