Double Penalized Quantile Regression for the Linear Mixed Effects Model  

作　　者：LI Hanfang LIU Yuan LUO Youxi 

机构地区：[1]School of Science,Hubei University of Technology,Wuhan 430068,China [2]School of Mathematics and Statistics,Central China Normal University,Wuhan 430079,China [3]Rollins School of Public Health,Emory University,Atlanta 30322,USA

出　　处：《Journal of Systems Science & Complexity》2020年第6期2080-2102,共23页系统科学与复杂性学报（英文版）

基　　金：the National Social Science Fund under Grant No.17BJY210。

摘　　要：This paper proposes a double penalized quantile regression for linear mixed effects model,which can select fixed and random effects simultaneously.Instead of using two tuning parameters,the proposed iterative algorithm enables only one optimal tuning parameter in each step and is more efficient.The authors establish asymptotic normality for the proposed estimators of quantile regression coefficients.Simulation studies show that the new method is robust to a variety of error distributions at different quantiles.It outperforms the traditional regression models under a wide array of simulated data models and is flexible enough to accommodate changes in fixed and random effects.For the high dimensional data scenarios,the new method still can correctly select important variables and exclude noise variables with high probability.A case study based on a hierarchical education data illustrates a practical utility of the proposed approach.

关 键 词：Double penalized fixed effects quantile regression random effects variable selection 

分 类 号：O212.1[理学—概率论与数理统计]