报告题目：Optimal Estimation for the Functional Cox Model

报告人：Xiao Wang, Purdue University, USA

时  间：2015年7月13日    下午4:00―5:00

地  点：东区管理科研楼  365英国上市官网1218室

内容提要：

Functional covariates are common in many medical, biodemographic, and neuroimaging studies. The aim of this talk is to study functional Cox models with right-censored data in the presence of both functional and scalar covariates. We study the asymptotic properties of the maximum partial likelihood estimator and establish the asymptotic normality and efficiency of the estimator of the finite-dimensional estimator. Under the framework of  reproducing kernel Hilbert space, the estimator of the coefficient function for a functional covariate achieves the minimax optimal rate of convergence under a weighted $L_2$-risk.