报告题目：Factor-Augmented Transformation Models for Interval-Censored Failure Time Data
报 告 人：李树威 副教授 广州大学
报告时间：2023年6月20日 13：30-14：30
报告地点：腾讯会议 ID：650387243
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This study proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates.We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event.Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation.We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative study is provided. An R package ICTransCFA is also available for practitioners.
报告人简介：李树威，广州大学统计系副教授、研究生导师。研究领域为生物统计、生存分析、纵向数据等。担任多个学会的常务理事和理事，主持国家自然科学基金青年基金等项目，发表多篇SCI论文。
