Adaptive Local Linear Quantile Regression

作     者：Yu-nan Su Mao-zai Tian 

作者机构：Center for Applied Statistics School of Statistics Remin University of China Beijing 100872 China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2011年第27卷第3期

页      面：509-516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] 070104[理学-应用数学] 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 (No.10871201) the Major Project of Humanities Social Science Foundation of Ministry of Education (No. 08JJD910247) Key Project of Chinese Ministry of Education (No.108120) Beijing Natural Science Foundation (No. 1102021) Graduate Research Foundation of Ren Min University of China (Adaptive Composite Quantile Regression Model and Bootstrap Confidence Interval Theory and Applications (No.11XNH108)) 

主　　题：quantile regression local linear regression adaptive smoothing automatic choice of window size Robustness 

摘      要：In this paper we propose a new method of local linear adaptive smoothing for nonparametric conditional quantile regression. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on a simulated example and compare it with other methods. The simulation results demonstrate a reasonable performance of our method proposed especially in situations when the underlying image is piecewise linear or can be approximated by such images. Generally speaking, our method outperforms most other existing methods in the sense of the mean square estimation （MSE） and mean absolute estimation （MAE） criteria. The procedure is very stable with respect to increasing noise level and the algorithm can be easily applied to higher dimensional situations.