关于举行西南大学吕晶副教授学术报告会的通知

报告题目：Subject-wise empirical likelihood inference for robust jointmean-covariance model with longitudinal data

报 告 人：吕晶 副教授(西南大学)

报告时间：2019年11月22日（星期五）下午17:00-18:00                

报告地点：4号楼318室

邀  请 人：王绍臣 博士

欢迎广大师生前往！

数学学院

2019年11月21日

报告摘要：

In longitudinal studies, one of the biggest challenges is how to obtain a good estimator of covariance matrix to improve the estimation efficiency of the mean regression coefficients. Meanwhile, one outlier in a subject level may generate multiple outliers in the sample due to repeated measurements. To solve these problems, this paper develops a robust joint mean–covariance model using the bounded exponential score function and modified Cholesky decomposition. The motivation for this new procedure is that it enables us to achieve high effectiveness and robustness simultaneously by introducing an additional tuning parameter γ which can be automatically selected using a data-driven procedure. In addition, we propose a subject-wise empirical likelihood to construct the confidence intervals/regions for the mean regression coefficients. Furthermore, under some mild conditions, we have established asymptotic theories of the proposed procedures. Finally, simulation studies are constructed to evaluate the finite sample performance of the proposed methods. A practical progesterone example is used to demonstrate the superiority of our proposed method.