Composite Quantile Estimation in Partial Functional Linear Regression Model Based on Polynomial Spline

• 作者: Ping YU Ting LI Zhong Yi ZHU Jian Hong SHI
• 来源期刊: 数学学报（英文版）
• 年，卷（期）: 2021,（10）
• 页码范围: 1627-1644
• 页数: 18页
• 语种: 英文

摘要

In this paper,we consider composite quantile regression for partial functional linear re-gression model with polynomial spline approximation.Under some mild conditions,the convergence rates of the estimators and mean squared prediction error,and asymptotic normality of parameter vec-tor are obtained.Simulation studies demonstrate that the proposed new estimation method is robust and works much better than the least-squares based method when there are outliers in the dataset or the random error follows heavy-tailed distributions.Finally,we apply the proposed methodology to a spectroscopic data sets to illustrate its usefulness in practice.