报告题目：Analysis of longitudinal data with semiparametric varying-coefficient mean-covariance models

报 告 人：赵彦勇 副教授 南京审计大学

报告时间：2021年11月12日 11:30-12:30

报告地点：腾讯会议 ID：221 156 831  会议密码：1112

校内联系人：朱复康 fzhu@jlu.edu.cn

报告摘要：The efficient estimation of regression coefficients in the longitudinal data analysis requires a correct specification of the covariance structure. Existing approaches usually focus on modeling the mean with specification of certain covariance structures, which may lead to inefficient or biased estimators of parameters in the mean if misspecification occurs. In this article, we propose a novel data-driven approach based on semiparametric varying-coefficient models to model the mean and the covariance simultaneously, motivated by the modified Cholesky decomposition. An iterative estimation method is proposed, consisting of an orthogonality-based technique for parameters, an adaptive jump-preserving estimation method for varying coefficients, a modification of local linear smoothing technique for the autoregressive coefficient function, and a kernel smoothing technique for the variance function. Theoretical properties of the resulting estimators including uniform consistency and asymptotic normality are explicitly studied under certain mild conditions. Simulation studies are carried out to evaluate the efficacy of the proposed methods, and an analysis of a real data example is provided for illustration.

报告人简介：赵彦勇，南京审计大学统计与数据科学学院副教授、硕士生导师。2016年于东南大学获统计学博士学位，随后入职南审任教。主要从事统计学及其与数据科学领域的交叉研究和实际应用。研究兴趣包括：复杂数据分析、统计机器学习、非/半参数模型、跳跃回归分析等，在国内外统计学和数学领域期刊上发表论文30余篇，主持国家自然科学基金面上项目、青年项目，全国统计科学研究项目重点项目等。