This article proposes a robust method for analysing longitudinal continuous responses with informative dropouts and potential outliers by using the multivariate t -distribution. We specify a dropout mechanism and a missing covariate distribution and incorporate them into the complete data log-likelihood. Unlike the existing approaches which mainly focus on the inference of regression mean and dropouts process, our approach aims to reveal the dynamics in the location function, marginal scale function and association by joint parsimonious modeling the location and dependence structure. A parametric fractional imputation algorithm is developed to speed up the computation associated with the EM algorithm for maximum likelihood estimation with missing data. The resulting estimators are shown to be consistent and asymptotically normally distributed. Data examples and simulations demonstrate the effectiveness of the proposed approach.

中文翻译：

---

具有信息缺失的纵向数据的鲁棒联合建模方法

本文提出了一种可靠的方法，该方法通过使用多元 t 分析具有信息缺失和潜在离群值的纵向连续响应。 -分配。我们指定一个退出机制和一个缺失的协变量分布，并将其合并到完整的数据对数似然中。与现有的方法主要集中于回归均值和辍学过程的推论不同，我们的方法旨在通过联合简约地模拟位置和依存结构来揭示位置函数，边际尺度函数和关联的动力学。开发了参数分数插补算法，以加快与EM算法相关的计算，从而在丢失数据的情况下实现最大似然估计。结果表明，估计量是一致的，并且渐近正态分布。数据示例和仿真证明了该方法的有效性。