摘要：In longitudinal data analysis, it is crucial to understand the dynamic of the covariance matrix of repeated measurements and correctly model it in order to achieve efficient estimators of the mean regression parameters. It is well known that any incorrect covariance matrices can result in inefficient estimators of the mean regression parameters. In this article, we propose an empirical likelihood based method which combines the advantages of different dynamic covariance modeling approaches. The effectiveness of the proposed approach is demonstrated by an anesthesiology dataset and some simulation studies..
关键词：empirical likelihood; longitudinal data analysis; maximum likelihood; modified Cholesky decomposition; multiple covariance models
