Double-Penalized Quantile Regression in Partially Linear Models

• 作者: Yunlu Jiang
• 来源期刊: 统计学期刊(英文)
• 学科: 数学
• 关键词: QUANTILE Regression PARTIALLY LINEAR MODEL Heavy-Tailed DISTRIBUTION
• 年，卷（期）: 2015,（2）
• 页码范围: 158-164
• 页数: 7页
• 分类号: O21

摘要

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.