Quantile Regression under Truncated,Censored and Dependent Assumptions  

作　　者：Chang-sheng LIU Yun-jiao LU Si-li NIU 

机构地区：[1]School of Mathematics and Physics,Henan University of Urban Construction,Pingdingshan 467036,China [2]School of Mathematical Sciences,Tongji University,Shanghai 200092,China

出　　处：《Acta Mathematicae Applicatae Sinica》2025年第2期479-497,共19页应用数学学报(英文版)

基　　金：supported by the National Natural Science Foundation of China(12071348);the Key Scientific Research Foundation of Henan Educational Committee(24A110001);Key Laboratory of Intelligent Computing and Applications(Ministry of Education),Tongji University,China.

摘　　要：In this paper,we focus on the problem of nonparametric quantile regression with left-truncated and right-censored data.Based on Nadaraya-Watson(NW)Kernel smoother and the technique of local linear(LL)smoother,we construct the NW and LL estimators of the conditional quantile.Under strong mixing assumptions,we establish asymptotic representation and asymptotic normality of the estimators.Finite sample behavior of the estimators is investigated via simulation,and a real data example is used to illustrate the application of the proposed methods.

关 键 词：asymptotic normality asymptotic representation nonparametric quantile regression truncated and censored -MIXING 

分 类 号：O212[理学—概率论与数理统计]