Composite Quantile Regression Estimation for Left Censored Response Longitudinal Data  

作　　者：Li-qun XIAO Zhan-feng WANG Yao-hua WU 

机构地区：[1]School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China [2]Department of Statistics and Finance, The School of Management, University of Science and Technology of China, Hefei 230026, China

出　　处：《Acta Mathematicae Applicatae Sinica》2018年第4期730-741,共12页应用数学学报（英文版）

基　　金：Supported in part by the National Natural Science Foundation of China under(Grant No.11601097 and 11471302);the State Key Program of National Natural Science of China(Grant No.11231010)

摘　　要：For left censored response longitudinal data, we propose a composite quantile regression estimator（CQR） of regression parameter. Statistical properties such as consistency and asymptotic normality of CQR are studied under relaxable assumptions of correlation structure of error terms. The performance of CQR is investigated via simulation studies and a real dataset analysis.For left censored response longitudinal data, we propose a composite quantile regression estimator（CQR） of regression parameter. Statistical properties such as consistency and asymptotic normality of CQR are studied under relaxable assumptions of correlation structure of error terms. The performance of CQR is investigated via simulation studies and a real dataset analysis.

关 键 词：longitudinal data left censored quantile regression randomly weighting 

分 类 号：O212.1[理学—概率论与数理统计] TQ028.8[理学—数学]