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.