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 Ping YU;Ting LI;Zhong Yi ZHU;Jian Hong SHI

作者机构：School of Mathematics and Computer ScienceShanxi Normal UniversityLinfen 041000P.R.China School of Statistics and ManagementShanghai University of Finance and EconomicsShanghai 200433P.R.China Department of StatisticsFudan UniversityShanghai 200433P.R.China 

出 版 物：《Acta Mathematica Sinica,English Series》 (数学学报（英文版）)

年 卷 期：2021年第37卷第10期

页      面：1627-1644页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：Supported by the National Natural Science Foundation of China(Grant Nos.11671096,11690013,11731011 and 12071267) the Natural Science Foundation of Shanxi Province,China(Grant No.201901D111279) 

主　　题：Asymptotic normality composite quantile regression functional data analysis polynomial spline rates of convergence 

摘      要：In this paper,we consider composite quantile regression for partial functional linear regression model with polynomial spline *** some mild conditions,the convergence rates of the estimators and mean squared prediction error,and asymptotic normality of parameter vector are *** 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 ***,we apply the proposed methodology to a spectroscopic data sets to illustrate its usefulness in practice.