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Generalized functional partial linear varying-coe_cient model for asynchoronous longitudinal data
时间:2019年11月07日 14:09 点击数:

报告人:朱仲义

报告地点:数学与统计学院415室

报告时间:2019年11月09日星期六16:00-17:00

邀请人:郭建华、郑术蓉

报告摘要:

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.

主讲人简介:

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。

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