报告题目：Conditional feature screening for mean and variance functions in high dimensional models
活动时间：5月17日15：00-16:00
活动地点：统计学院资料室
主讲人：胡琴琴
报告摘要：The existing methods for feature screening focus mainly on the mean function of regression models. The variance function, however, plays an important role in statistical theory and application. We thus investigate feature screening for mean and variance functions with multiple-index framework in high dimensional regression models. Notice that some information about predictors can be known in advance from previous investigations and experience, for example, a certain set of predictors is related to the response. Based on the conditional information, together with empirical likelihood, we propose conditional feature screening procedures. Our methods can consistently estimate the sets of active predictors in the mean and variance functions. It is interesting that the proposed screening procedures can avoid estimating the unknown link functions in the mean and variance functions, and moreover, can work well in the case of high correlation among the predictors without iterative algorithm. Therefore, our proposal is of computational simplicity. Furthermore, as a conditional method, our method is robust to the choice of the conditional set. The theoretical results reveal that the proposed procedures have sure screening properties. The attractive finite sample performance of our method is illustrated in simulations and a real data application.
报告人简介：胡琴琴, 山东大学（威海）数学与统计学院副教授。2015年毕业于山东大学金融研究院，获博士学位。研究兴趣主要有高维数据分析和非参半参建模。2012.06-2013.08作为研究学者赴美国奥本大学访学交流。目前主持国家自然基金青年项目等多项科研项目。在国际统计学权威期刊Scandinavian Journal of Statistics、Computational Statistics and Data Analysis等发表多篇学术论文。
