An efficient and robust inference method based on empirical likelihood in longitudinal data analysis

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.

Automated summary

This paper presents a new efficient and robust inference method by combining the robust generalized estimating equations and the empirical likelihood method for longitudinal data analysis. The method constructs robust auxiliary random vectors based on a bounded exponential score function and leverage-based weights to achieve robustness against outliers in both the response and covariate domains. Moreover, the tuning parameter in the exponential score function can be automatically selected using the observed data. Simulation studies and a real data analysis were conducted to demonstrate the performance of the proposed method.