报告地点：行健楼学术活动室665
邀请人：高启兵教授
报告摘要：Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications such as in large epidemiological studies or multi-site cohort studies. Generalized estimating equations (GEE) is one of the popular statistical methods for analyzing this kind of data, because they can handle many types of unmeasured dependence between outcomes. Collecting large number of the highly stratified or correlated responses data is a time-consuming job. Thus, to have a more aggressive sampling strategy that can accelerate this process, as active learning methods in machine learning literature, is always beneficial. In this paper, we integrate the adaptive sampling and variable selection features into a sequential procedure for modeling the correlated responses data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized data and real data sets to demonstrate the usefulness of our method.