Estimation in quantile regression models for correlated data with diverging number of covariates and large cluster sizes

作     者：Zhao, Weihua Zhang, Xiaoyu Yuen, Kam Chuen Li, Rui Lian, Heng 

作者机构：Nantong Univ Sch Sci Nantong Peoples R China Univ Hong Kong Dept Stat Pok Fu Lam Hong Kong Peoples R China Shanghai Univ Int Business & Econ Sch Stat & Informat Shanghai 201620 Peoples R China City Univ Hong Kong Dept Math Kowloon Tong Hong Kong Peoples R China 

出 版 物：《COMMUNICATIONS IN STATISTICS-THEORY AND METHODS》 (统计学通讯：理论与方法)

年 卷 期：2023年第52卷第4期

页      面：1012-1038页

核心收录：

学科分类：0202[经济学-应用经济学] 02[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）] 

基　　金：National Social Science Fund of China [17BTJ025] 

主　　题：Clustered data diverging dimensionality induced smoothing quadratic inference functions quantile regression 

摘      要：In many data analytic problems, repeated measurements with a large number of covariates are collected and conditional quantile modeling for such correlated data are often of significant interest, especially in medical applications. We propose a quadratic inference functions based approach to take into account the correlations within clusters and use smoothing to make the objective function amenable to computation. We show that the asymptotic properties of the estimators are the same whether or not smoothing is applied, established in the diverging p, large n setting. The cluster sizes are also allowed to diverge with sample size n. Simulation results are presented to demonstrate the effectiveness of the proposed estimator by taking into account the within-cluster correlations and we use a longitudinal data set to illustrate the method.