Weighted quantile regression for longitudinal data using empirical likelihood
Weighted quantile regression for longitudinal data using empirical likelihood作者机构:School of Basic Science Changchun University of Technology Department of Mathematics Washington University in St.Louis School of Mathematics Jilin University
出 版 物:《Science China Mathematics》 (中国科学:数学(英文版))
年 卷 期:2017年第60卷第1期
页 面:147-164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 National Natural Science Foundation of China (Grant Nos. 11401048, 11301037, 11571051 and 11201174) the Natural Science Foundation for Young Scientists of Jilin Province of China (Grant Nos. 20150520055JH and 20150520054JH)
主 题:empirical likelihood estimating equation influence function longitudinal data weighted quantile regression
摘 要:This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood(EL). This approach efficiently incorporates the information from the conditional quantile restrictions to account for within-subject correlations. The resulted estimate is computationally simple and has good performance under modest or high within-subject correlation. The efficiency gain is quantified theoretically and illustrated via simulation and a real data application.