时 间：2019年6月17日(周一)下午14:30-15:30
地 点：中北校区理科大楼A1514会议室
题 目：GFPV: Regression Analysis of Asynchronous Longitudinal Functional and Scalar Data
报告人：朱仲义 复旦大学 教授
摘 要：
Many modern large-scale longitudinal neuroimaging studies have collected/are collecting asynchronous scalar and functional variables that are measured at distinct time points. The analyses of temporally asynchronous functional and scalar variables pose major technical challenges to all existing statistical approaches. We propose a class of generalized functional partial-linear varying coefficient (GFPV) models to appropriately deal with these challenges through introducing both scalar and functional coefficients of interest and using kernel weighting methods. Penalized kernel-weighted estimating equations are designed to compute the estimators of scalar and functional coefficients. We establish the theoretical properties of scalar and functional coefficient estimates including consistency, convergence rate, prediction accuracy, and limit distributions. We also propose a bootstrap method to test the nullity of both parametric and functional coefficients and establish the bootstrap consistency. Simulation studies and the analysis of the Alzheimer's Disease Neuroimaging Initiative study are used to assess the finite sample performance of our proposed approach.
主讲人简介：
朱仲义：复旦大学统计系教授，博士生导师。Elected Member of the ISI（国际数理统计学会）、《中国科学：数学》杂志编委。曾任中国概率统计学会第八、九届副理事长，国际著名杂志《Statistica Sinica》副主编，《应用概率统计》、《数理统计与管理》杂志编委，中国统计教材编审委员会委员。