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 const...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.展开更多
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.
文摘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.
