题    目：高维数据变量选择的统计方法
主讲人： 张敏 教授 (美国普渡大学)
时    间：2017年12月15日 上午10:30
地    点：主楼418

主讲人介绍：
       Dr. Min Zhang is a professor of Statistics at Purdue University. She received her MD from Hebei Medical Unviersity, PhD in Neurobiology from Peking Univeristy Health Science Center, and PhD in Biometry from Cornell University. Her current research focuses on developing statistical methods that can extract information from biomedical big data more efficiently and effectively, including methods for quantitative trait loci mapping and genome-wide association studies. Recently she is working on variable selction methods that can applied to systems biology and precision medicine.

内容介绍：
       We developed a variable selection method, namely penalized orthogonal components regression, to simultaneously model multiple response variables for data with large number of predictors but small sample size. Orthogonal components are sequentially constructed to maximize their correlation to the response residuals. A new penalization framework through empirical Bayes thresholding is employed to efficiently identify sparse predictors of each component. The method can group highly correlated predictors and is computationally efficient. Extensive computer simulation studies show the superior performance of the proposed method, and it has been applied to real data collected in biomedical studies.

（承办：管理科学与物流系，科研与学术交流中心）