1.School of Basic Science,Changchun University of Technology,Changchun,China;2.Department of Mathematics,Washington University in St. Louis,St. Louis,USA;3.School of Mathematics,Jilin University,Changchun,China
Abstract:
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.