题目：Semiparametric Structural Equation Models with  Interval-Censored Data

报告人:李树威  副教授（广州大学）

摘要：Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social, or genomic studies. However, the existing methodology for structural equation modeling is not concerned with interval-censored data, a type of coarse survival data arising typically from periodic examinations for the occurrence of asymptomatic disease. The present study aims to fill this gap and provide a flexible semiparametric structural equation modeling framework. A general class of factor-augmented transformation models is proposed to model the interval-censored outcome of interest in the presence of latent risk factors. An expectation-maximization algorithm is subtly designed to conduct the nonparametric maximum likelihood estimation. Furthermore, the asymptotic properties of the proposed estimators are established by leveraging the empirical process theory. The numerical results obtained from extensive simulations and an application to the Alzheimer’s disease data set demonstrate the proposed method’s empirical performance and practical utility.

报告人简介：李树威, 统计学博士, 现任广州大学经济与统计学院副教授。2017年6月博士毕业于吉林大学统计系。主要研究方向为生物统计、应用统计，相关研究成果发表在《Biometrika》、《Biometrics》、《Statistics in Medicine》、《Statistical Methods in Medical Research》、《Scandinavian Journal of Statistics》、《Statistica Sinica》、《Journal of Computational and Graphical Statistics》等期刊上。主持国家自然科学基金青年项目、广东省自然科学基金面上项目、广州市科技局项目等。

报告时间：2024年5月16号14:00-15:00

报告地点：腾讯会议：149-476-503

联系人：郭文雯