报告题目：Generalized Additive Coefficient Models with High-dimensional Covariates for GWAS
报 告 人：Hua Liang 教授 美国乔治华盛顿大学
报告时间：2019年7月10日下午1:30-2:10
报告地点：数学楼一楼第一报告厅
报告摘要:
In the low-dimensional case, the generalized additive coefficient model (GACM) proposed 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 group-wise 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. Furthermore, 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：
Hua Liang是美国乔治华盛顿大学统计系统计和生物统计学教授（2013 ---至今）。 Liang教授于1992年获得中国科学院系统科学研究所数学统计学博士学位，并于2001年获得美国德州农机大学统计学博士学位。他是St. Jude儿童研究医院的助理教授（2002-2005），罗切斯特大学医学中心的副教授（2005-2009）和教授（2009-2013）。Liang教授致力于半参数回归，纵向数据的混合效应模型，缺失数据，测量误差模型，变量选择和HIV动态模型等方向的研究。他获得了两项美国国立卫生研究院的RO1，一项T32和五项NSF研究经费。他是美国统计协会，国际数理统计协会，皇家统计学会会员和国际统计协会的当选会员。Biometrics, Electronic Journal of Statistics和JASA的副主编。
