学术报告：蒋学军 南方科技大学

主讲人：蒋学军 南方科技大学

题 目： Penalized weighted generalized estim-

ation equations for high-dimensional longitudi-

nal data with informative cluster size

时间：2025年10月18日 14:00-15:30

地点：VSport体育官网新校园 B514

摘要：High-dimensional longitudinal data have become increasingly prevalent in recent studies, and penalized generalized estimating equations (GEEs) are often used to model such data. However, the desirable properties of the GEE method can break down when the outcome of interest is associated with cluster size, a phenomenon known as informative cluster size. In this article, we address this issue by formulating the effect of informative cluster size and proposing a novel weighted GEE approach to mitigate its impact, while extending the penalized version for high-dimensional settings. We show that the penalized weighted GEE approach achieves consistency in both model selection and estimation. Theoretically, we establish that the proposed penalized weighted GEE estimator is asymptotically equivalent to the oracle estimator, assuming the true model is known. This result indicates that the penalized weighted GEE approach retains the excellent properties of the GEE method and is robust to informative cluster sizes, thereby extending its applicability to highly complex situations. Simulations and a real data application further demonstrate that the penalized weighted GEE outperforms the existing alternative methods.