报告题目：变系数模型的稀疏降秩回归

报告时间：6月9日（周一）下午4：00到5：00

报告地点： 5号楼107

报  告人：南洋理工大学练恒教授

 Title: Sparse reduced rank regression for varying coefficient models
Abstract:
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：

 Prof. Heng Lianobtained his B.S. at University of Science and Technology of China in 2000, and completed his M.S. and Ph.D. at BrownUniversityin USA in 2005 and 2007, respectively.

He is now an assistant Professor at NanyangTechnological University. He has published more than 20 papers in many statistical journals of high quality, such as Journal of the Royal Statistical Society(Series B)（统计四大期刊之一）,Biometrics（生物统计顶级期刊）, Journal of Business and Economic Statistics（开云全站中国有限公司顶级期刊）, Statistica Sinica, Statistics and Computing, Econometric Theory, etc.He is a frequent reviewer for more than 30 statistical  journals.

   His current research interests include semiparametric statistics , high-dimensional data anlysis, pattern recognition, Bayesian analysis, functional data analysis, etc.

     Detailed information about Prof. Heng Lian can be found in http://www.ntu.edu.sg/home/HengLian/allpubs.htm.