Abstract

This paper presents a new efficient and robust inference method by combing the robust generalized estimating equations and the well-known empirical likelihood method in longitudinal data analysis. Based on a bounded exponential score function and leverage-based weights, robust auxiliary random vectors are constructed to achieve robustness against outliers both in the response and the covariate domains. Moreover, the additional tuning parameter in the exponential score function can be automatically selected by the observed data. Finally, some simulation studies and a real data analysis are carried out to demonstrate the performances of the proposed method.

中文翻译：

---

纵向数据分析中基于经验似然的高效稳健推理方法

摘要

本文将稳健的广义估计方程与著名的纵向数据分析经验似然法相结合，提出了一种新的高效稳健的推理方法。基于有界指数得分函数和基于杠杆的权重，构建鲁棒的辅助随机向量以实现对响应域和协变量域中异常值的鲁棒性。此外，指数得分函数中的附加调整参数可以由观测数据自动选择。最后，进行了一些模拟研究和真实数据分析，以证明所提出方法的性能。