报告题目：Variable Selection for High-Dimensional Single Index ODE Model

报告人： 鹿涛

报告时间地点：2017年6月30日上午10：00-10:45，理科楼407

摘要：

The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a single index ODE model, coupled with ODE estimation methods and SCAD techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The asymptotic properties of the proposed method are established in ``large p, small n" setting. Simulation studies are performed to validate the proposed approach. An application example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method.

报告人简介：

鹿涛教授本科毕业于中国科技大学数学系，博士毕业于美国罗彻斯特大学统计系，任美国纽约州立大学，内华达大学数学统计系助理教授，副教授，博导。鹿教授的研究工作是发展基于微分方程的统计模型方法，及其应用。近五年来，发表包括统计学权威期刊Journal of the American Statistical Association，Biometrics, Statistics in Medicne, Statistical Methods in Medical Research,  Annals of Applied Statistics论文30余篇（多为第一及通讯作者）。研究成果获得同行的广泛关注与认可，受邀在“10th International Conference on Health Policy Statistics”, “Statistics and Computational Interface to Big Data”等国际会议上作邀请报告。鹿教授目前担任统计学期刊Journal of Biopharmaceutical Statistics 和Biometrical Journal的副主编以及PLOS ONE和PEERJ的主编, 并担任美国宇航局宇航员健康研究项目生物统计评审。