報告題目：Generalized Additive Coefficient Models with High-dimensional Covariates for GWAS
報 告 人：Hua Liang 教授 美國喬治華盛頓大學
報告時間：2019年7月10日下午1:30-2:10
報告地點：數學樓一樓第一報告廳
報告摘要:
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.
報告人簡介：
Hua Liang是美國喬治華盛頓大學統計系統計和生物統計學教授（2013 ---至今）。 Liang教授于1992年獲得中國科學院系統科學研究所數學統計學博士學位，并于2001年獲得美國德州農機大學統計學博士學位。他是St. Jude兒童研究醫院的助理教授（2002-2005），羅切斯特大學醫學中心的副教授（2005-2009）和教授（2009-2013）。Liang教授緻力于半參數回歸，縱向數據的混合效應模型，缺失數據，測量誤差模型，變量選擇和HIV動态模型等方向的研究。他獲得了兩項美國國立衛生研究院的RO1，一項T32和五項NSF研究經費。他是美國統計協會，國際數理統計協會，皇家統計學會會員和國際統計協會的當選會員。Biometrics, Electronic Journal of Statistics和JASA的副主編。
