报告人：谌自奇
报告地点：数学与统计学院415报告厅
报告时间：2018年09月17日星期一16:00-17:00
邀请人：
报告摘要：
The aim of this paper is to develop a new framework of surface functional models for surface functional data which contains repeated observations in both the domains. The primary interest in our problem is to investigate the relationship between a response and the two domains, where the numbers of observations in both domains within a subject may be diverging. We estimate the mean function based on local linear smoothers. Unprecedented complexity presented in the surface functional models, such as possibly distinctive sampling designs and the dependence between the two domains, makes the theoretical investigation challenging. We are able to provide a comprehensive investigation of the asymptotic properties of the mean function estimator based on a general weighing scheme. Moreover, we can mathematically categorize the surface data into nine cases according to the sampling designs of both the domains, essentially based on the relative order of the number of observations in each domain to the sample size. We derive the specific asymptotic theories and optimal bandwidth orders in each of the nine sampling design cases under all the three weighing schemes. We also examine the finite-sample performance of the estimators through simulation studies.
主讲人简介：
中南大学副教授，研究生导师；中南大学和安德森癌症研究中心博士后。2012年毕业于东北师范大学概率与数理统计专业，在Journal of the American Statistical 等国际杂志上发表论文，且是Statistica Sinica， Scandinavion Journal of Statistics等杂志的审稿人；获2014年吉林省优秀博士学位论文，主持国家自然科学基金青年基金和面上基金各一项。 教育背景： 2006.9-2012.6，东北师范大学数学与统计学院，概率论与数理统计专业，硕博连读； 2002.9-2006.6，湖南师范大学数学与计算机科学学院，数学与应用数学专业，本科。
