报告题目：Separation of Covariates into Nonparametric and Parametric Parts in High-Dimensional Partially Linear Additive Models

时间：周四（5月22日）下午4点

地点：数学楼二楼学术报告厅

报告人：Hua Liang, Professor, Department of Statistics, George Washington University

报告摘要：Determining which covariates enter the linear part of a partially linear additive model is always challenging. This challenge becomes more serious when the number of covariates diverges with the sample size. In this paper,  we propose a double penalization based procedure to distinguish covariates that enter the nonparametric and parametric parts and to identify insignificant covariates simultaneously for the ``large p small n" setting. The procedure is shown to be consistent for model structure identification. That is, it can identify zero, linear and nonlinear components correctly. Moreover, the resulting estimators of the linear coefficients are shown to be asymptotically normal. We also discuss how to choose the penalty parameters and provide theoretical justification. We conduct extensive simulation experiments to evaluate the numerical performance of the proposed methods and analyze a gene data set for an illustration.

报告人：梁华，1992年中科院数学博士，1995年德国洪堡奖学金获得者，2001年美国农业军事大学统计学博士，现任美国乔治华盛顿大学统计系教授，统计顶级期刊JASA编委，美国统计协会会士，数理统计学院会士，国际统计学院会员。