Composite quantile regression for correlated data

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• 作者：Weihua Zhao^a ; Heng Lian^b ; ^{henglian@cityu.edu.hk} ; Xinyuan Song^c
• 关键词：Composite quantile regression ; Longitudinal data ; Quadratic inference function ; Weighted composite quantile regression
• 刊名：Computational Statistics & Data Analysis
• 出版年：2017
• 出版时间：May 2017
• 年：2017
• 卷：109
• 期：Complete
• 页码：15-33
• 全文大小：552 K
• 卷排序：109

文摘

This study investigates composite quantile regression estimation for longitudinal data on the basis of quadratic inference functions. By incorporating the correlation within subjects, the proposed CQRQIF estimator has the advantages of both robustness and high estimation efficiency for a variety of error distributions. The theoretical properties of the resulting estimators are established. Given that the objective function is non-smooth and non-convex, an estimation procedure based on induced smoothing is developed. It is proved that the smoothed estimator is asymptotically equivalent to the original estimator. The weighted composite quantile regression estimation is also proposed to improve the estimation efficiency further in some situations. Extensive simulations are conducted to compare different estimators, and a real data analysis is used to illustrate their performances.