报告题目：Sparse reduced rank regression for varying coefficient models 

报告时间：2014.6.24上午10:00-11:30 

报告地点：尚贤楼808 

报告主持人：来鹏 博士 

报告摘要： In genetic studies, not only can the number of predictors obtained from microarray measurements be extremely large, there can also be multiple response variables. Motivated by such a situation, we consider semiparametric dimension reduction methods in sparse multivariate regression models. Previous studies on joint variable and rank selection have focused on parametric models. We consider a more flexible varying-coefficient model which makes the investigation on nonlinear interactions and study of dynamic patterns possible for multivariate regression analysis. Spline approximation, rank constraints and concave group penalties are utilized for model estimation. Asymptotic oracle properties of the estimators are presented. We also propose a reduced-rank independence screening procedure to deal with the situation that the dimension of the covariates is so high that penalized estimation cannot be directly applied. Our proposed method is illustrated by simulation studies, and by an analysis of a real data example to identify genetic factors and evaluate their effects on multivariate responses under environmental influences. 

报告人简介：练恒，男，博士，助理教授，2000年获中国科技大学计算机科学与数学双学士学位，2005年获得美国布朗大学经济学和计算机科学双硕士学位，2007年获美国布朗大学应用数学博士学位，2007年至今在新加坡南洋理工大学物理和数学科学学院担任助理教授，主要研究方向：高维数据分析、非参数和半参数统计、函数型数据分析、贝叶斯分析、模式识别和机器学习等。