Composite quantile regression (CQR) is a powerful complement to the usual mean regression and becomes increasingly popular due to its robustness and efficiency. In longitudinal studies, it is necessary to consider the intra-subject correlation among repeated measures to improve the estimation efficiency. This paper proposes a new approach that uses copula to account for intra-subject dependence in CQR. By using the copula-based covariance matrix, efficient CQR estimating equations are constructed for the longitudinal data partial linear varying coefficient models. Our proposed new methods are flexible, and can provide efficient estimation. The properties of the proposed methods are established theoretically, and assessed numerically through simulation studies and real data analysis.

中文翻译：

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基于 Copula 和复合分位数回归的纵向数据估计方程

复合分位数回归 (CQR) 是对通常的均值回归的有力补充，并且由于其稳健性和效率而变得越来越流行。在纵向研究中，需要考虑重复测量之间的主体内相关性，以提高估计效率。本文提出了一种使用 copula 来解释 CQR 中主体内依赖性的新方法。利用基于copula的协方差矩阵，为纵向数据的偏线性变系数模型构造了有效的CQR估计方程。我们提出的新方法很灵活，可以提供有效的估计。所提出方法的特性在理论上建立，并通过模拟研究和实际数据分析进行数值评估。