报告地点：数学学院504会议室
报告题目：Imputed factor regression for high-dimensional block-wise missing data
报告内容：Abstract：With the prevalence of high-dimensional block-wise missing data in many fields such as biomedical, social and environmental sciences, it is important to address dimension reduction and prediction problems under block-wise missing data. Existing dimension reduction method is limited to block-wise missing data. We propose the two-step supervised factor-model imputation to handle block-wise missing and screen the important features, and then build factor regression model to predict response based on the imputed screened features. We show that the proposed estimators and predicting are consistent in some conditions, compare the proposed method with other approaches through simulation studies, and apply the approach to the ADNI data
唐年胜教授简介
唐年胜，云南大学教授、博士生导师，云南大学数学与统计学院院长。国家杰出青年科学基金获得者，2013年入选教育部“长江学者”奖励计划特聘教授；2015年入选国家百千万人才工程、云南省科技领军人才、云南省教学名师，获“国家有突出贡献中青年专家”荣誉称号；2016年遴选为Elected ISI Member（国际统计学会推选会员），2017年遴选为国际泛华统计学会“Board of Directors”。唐教授主要从事生物统计、贝叶斯统计、统计诊断、缺失数据分析、高维数据分析、生存数据分析的研究，并做出杰出的创新性工作；现分别兼任中国现场统计研究会常务理事、中国统计学会常务理事、中国概率统计学会常务理事、云南省应用统计学会理事长、中国现场统计研究会资源与环境统计分会副理事长。相关工作发表在《JASA》、《Biometrika》、《Biometrics》、《Biostatistics》、《Statistics in Medicine》、《Statistica Sinica》、《IEEE Transitions on Medical Imaging》、《Statistical Methods in Medical Research》等国际知名期刊上。
报告题目：Generalized functional partial linear varying-coefficient model for asynchoronous longitudinal data
报告内容： Motivated by the analysis of clinical studies, we propose a generalized functional partial linear varying-coefficient model for the analysis of longitudinal data where the observation times for the response and the functional covariate as well as the scalar covariates are mismatched within subjects. We represent the functional parameter by a rich truncated tensor product penalized B-spline basis. The estimators are obtained by the local kernel-weighted estimating equations with penalties, which are proposed to deal with the asynchronous longitudinal data. We examine the consistency of the estimators, and the convergence rate of the prediction error. Meanwhile, a bootstrap hypothesis testing method is developed to test the nullity of the coefficients. Simulation studies and an analysis of a real longitudinal functional dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) are used to demonstrate the performance of the proposed method.
朱仲义，复旦大学统计系教授，博士研究生导师；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；现为 Elected Member of the ISI（国际数理统计学会）；”中国科学：数学”杂志编委。专业研究方向为：保险精算；纵向数据(面板数据)模型；分位数回归模型等。主持完成国家自然科学基金四项、国家社会科学基金一项，作为子项目负责人完成国家自然科学基金重点项目一项。已经培养毕业博士研究生13名。目前主持国家自然科学基金重大项目子项目一项，重点项目子项目一项，面上项目一项。近几年发表论文100多篇(其中包括在国际四大统计顶级刊物等SCI论文六十多篇)。获得教育部自然科学二等奖一次。
