报告题目：Generalized Additive Coefficient Models with High-dimensional Covariates for Genome-wide Association Study

时间：2015年12月21（星期一）13：30-14：30

地点：学院南路校区，学术会堂603

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

摘要：

In the low-dimensional case, the generalized additive coefficient model (GACM) proposed by has been demonstrated to be a powerful tool for studying nonlinear interaction effects of variables. In this paper, we propose estimation and inference procedures for the GACM when the dimension of the variables is high. Specifically, we propose a groupwise penalization based procedure to distinguish significant covariates for the ``large p small n" setting. The procedure is shown to be consistent for model structure identification. Further, we construct simultaneous confidence bands for the coefficient functions in the selected model based on a refined two-step spline estimator. We also discuss how to choose the tuning parameters. To estimate the standard deviation of the functional estimator, we adopt the smoothed bootstrap method. We conduct simulation experiments to evaluate the numerical performance of the proposed methods and analyze an obesity data set from a genome-wide association study as an illustration.

报告人简介：

梁华教授为中央财经大学“手拉手”项目特聘教授，美国乔治华盛顿大学教授，中科院数理统计博士，Texas A&M University统计学博士。出版学术著作2部, 学术论文120多篇，其中20篇发表在国际统计学最顶级的四个期刊上（The Annals of Statistics, Biometrika, JASA, and JRSSB)。梁华教授共主持了6项美国国家科学基金会以及美国国立卫生研究院的研究项目，研究经费总计250万美元。另外还主持了1项海外港澳学者研究基金。他是美国统计学会(ASA), 国际数理统计学会(IMS), 英国皇家统计学会(RSS)会员。同时担任Journal of the American Statistical Association等多个国际著名统计学期刊的副主编。