Complexity of longitudinal data lies in the inherent dependence among measurements from same subject over different time points. For multiple longitudinal responses, the problem is challenging due to inter-trait and intra-trait dependence. While linear mixed models are popularly used for analysing such data, appropriate inference on the shape of the population cannot be drawn for non-normal data sets. We propose a linear mixed model for joint quantile regression of multiple longitudinal responses. We consider an asymmetric Laplace distribution for quantile regression and estimate model parameters by Monte Carlo EM algorithm. Nonparametric bootstrap resampling method is used for estimating confidence intervals of parameter estimates. Through extensive simulation studies, we investigate the operating characteristics of our proposed model and compare the performance to other traditional quantile regression models. We apply proposed model for analysing data from nutrition education programme on hypercholesterolemic children of the USA.

中文翻译：

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多纵向结果的联合分位数回归模型

纵向数据的复杂性在于，同一受试者在不同时间点的测量之间存在固有的依赖性。对于多个纵向响应，由于性状间和性状内的依赖性，该问题具有挑战性。尽管线性混合模型通常用于分析此类数据，但无法针对非正态数据集得出关于总体形状的适当推断。我们提出了一个线性混合模型，用于多个纵向响应的联合分位数回归。我们考虑了不对称拉普拉斯分布用于分位数回归，并通过蒙特卡洛EM算法估计模型参数。非参数自举重采样方法用于估计参数估计值的置信区间。通过广泛的模拟研究，我们研究了我们提出的模型的操作特性，并将其性能与其他传统的分位数回归模型进行了比较。我们将提出的模型用于分析来自美国高胆固醇血症儿童营养教育计划的数据。